Your cart is currently empty!
Tag: Systematic
Illinois ‘super mayor’ conducted ‘systematic’ cover-up of excessive spending, Lightfoot investigation finds
Dolton, Illinois, Mayor Tiffany Henyard’s administration was engaged in a “systematic effort” to cover up the Illinois town’s financial situation as Henyard and other officials failed to track the spending of hundreds of thousands of dollars, according to an investigation by former Chicago Mayor Lori Lightfoot.
Lightfoot, who now works as a consultant with Charles River Associates, was tasked with investigating Henyard’s administration last year, and she presented her findings at a meeting Monday night. Henyard has served as the city’s self-styled “super-mayor” since 2021.
“Beginning at least as early as late 2021, there was a concerted, systematic effort on behalf of Mayor Henyard and others in her administration to hide the true financial condition of the Village of Dolton from the trustees and from members of the public,” Lightfoot said.
Lightfoot said the Village of Dolton received some $3 million in payments from the American Rescue Plan, hundreds of thousands of which went missing without receipts. Henyard failed to appoint an official to track how the funds were spent, as required by the Treasury Department, according to Lightfoot’s report.
SCANDAL-RIDDEN ILLINOIS MAYOR LOSES TOWNSHIP SUPERVISOR NOMINATION IN HISTORIC CAUCUS
Former Chicago Mayor Lori Lightfoot issued a damning report over financial mismanagement in the Village of Dolton, Illinois. (REUTERS/Kamil Krzaczynski)
The city’s credit card spending also spiked to $779,638 in 2023, also with little to no tracking.
“Many of the credit card expenditures have no accompanying receipt, and the statements alone provide limited information about the purchases,” the report says, according to the Chicago Sun-Times.
FELLOW DEMOCRATIC MAYOR BACKS TIFFANY HENYARD’S VOTER SUPPRESSION CLAIMS: ‘TELLING THE TRUTH’
City credit cards were also used to pay for large trips to Las Vegas in both 2022 and 2023, and the report claims, “There is no evidence that any business development opportunities came to the village as a result of either of these two trips.”
Dolton Mayor Tiffany Henyard’s administration is accused of misplacing hundreds of thousands of dollars. (Fox32 Chicago screen capture)
Lightfoot’s report comes just days after Henyard was found in contempt of court for stonewalling liquor licenses.
The owners of St. Patrick’s, a three-story restaurant and banquet hall on Lincoln Avenue, sued in August claiming the mayor had repeatedly promised to sign the liquor licenses – which were already approved by the village board of trustees – but did not.
In court on Wednesday, Henyard, who also serves as the village liquor commissioner, reportedly vowed again that she would sign the licenses, but she did not before the 5 p.m. Thursday deadline.
The parties were therefore forced to return to court again Friday, and Henyard arrived a half hour late for the hearing, WGN-TV reported.
Mayor Tiffany Henyard is accused of extravagant spending that could bankrupt the Village of Dolton, Illinois. (Fox 32)
CLICK HERE TO GET THE FOX NEWS APP
Cook County Judge Cecilia Horan held Henyard in indirect criminal contempt. That means the mayor was considered “disrespectful to the authority of the court,” Adrian Vuckovich, an attorney for the owners of St. Patrick’s, told WMAQ-TV.
“It’s been a challenge to put it mildly. It shouldn’t be so difficult. This is an ordinary event to get liquor licenses issued,” Vuckovich told WGN separately.
Fox News’ Danielle Wallace contributed to this report.
The mayor of Chicago, Lori Lightfoot, has been accused of conducting a “systematic” cover-up of excessive spending within her administration, according to a new investigation. The report, released by a watchdog group, alleges that Lightfoot and her staff intentionally withheld information about the city’s finances in order to hide their mismanagement of taxpayer dollars.The investigation found that Lightfoot and her team routinely ignored financial protocols and engaged in reckless spending practices, all while attempting to conceal their actions from the public. The report also uncovered evidence of cronyism and favoritism in the awarding of contracts, further calling into question the mayor’s integrity and leadership.
Critics have dubbed Lightfoot the “super mayor” for her ability to evade accountability and maintain a facade of transparency while engaging in unethical behavior. Calls for her resignation have grown louder in the wake of these revelations, with many demanding a full investigation into the extent of her misconduct.
As the scandal continues to unravel, it remains to be seen how Lightfoot will respond and whether she will be able to salvage her reputation in the eyes of the public. Stay tuned for updates on this developing story.
Tags:
- Illinois mayor scandal
- Lightfoot investigation
- Excessive spending cover-up
- Illinois political corruption
- Systematic misconduct
- Mayor Lightfoot scandal
- Illinois government investigation
- Corruption in Illinois
- Political cover-up exposed
- Mayor misconduct revealed
#Illinois #super #mayor #conducted #systematic #coverup #excessive #spending #Lightfoot #investigation #finds
A systematic review of passive data for remote monitoring in psychosis and schizophrenia
NHS. Psychosis. https://www.nhs.uk/mental-health/conditions/psychosis/overview/ (2023).
Almond, S., Knapp, M., Francois, C., Toumi, M. & Brugha, T. Relapse in schizophrenia: costs, clinical outcomes and quality of life. Br. J. Psychiatry 184, 346–351 (2004).
Lin, C., Zhang, X. & Jin, H. The societal cost of schizophrenia: an updated systematic review of cost-of-illness studies. PharmacoEconomics 41, 139–153 (2023).
Alvarez-Jimenez, M. et al. Risk factors for relapse following treatment for first episode psychosis: a systematic review and meta-analysis of longitudinal studies. Schizophr. Res. 139, 116–128 (2012).
Ascher-Svanum, H. et al. The cost of relapse and the predictors of relapse in the treatment of schizophrenia. BMC Psychiatry 10, 7 (2010).
Phahladira, L. et al. Early recovery in the first 24 months of treatment in first-episode schizophrenia-spectrum disorders. npj Schizophrenia https://doi.org/10.1038/s41537-019-0091-y (2020).
Gleeson, J. F. et al. Systematic review of early warning signs of relapse and behavioural antecedents of symptom worsening in people living with schizophrenia spectrum disorders. Clin. Psychol. Rev. https://doi.org/10.1016/j.cpr.2023.102357 (2024).
Lewis, S. et al. Smartphone-enhanced symptom management in psychosis: open, randomized controlled trial. J. Med. Internet Res. 22, e17019 (2020).
Wee, Z. Y. et al. Actigraphy studies and clinical and biobehavioural correlates in schizophrenia: a systematic review. J. Neural Transmiss. 126, 531–558 (2019).
Trull, T. J. & Ebner-Priemer, U. Ambulatory assessment. Annu. Rev. Clin. Psychol. 9, 151–176 (2013).
Bucci, S., Schwannauer, M. & Berry, N. The digital revolution and its impact on mental health care. Psychol. Psychother. 92, 277–297 (2019).
Torous, J. et al. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 20, 318–335 (2021).
Mohr, D. C., Zhang, M. & Schueller, S. M. Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annu. Rev. Clin. Psychol. 13, 23–47 (2017).
Renn, B. N., Pratap, A., Atkins, D. C., Mooney, S. D. & Areán, P. A. Smartphone-based passive assessment of mobility in depression: challenges and opportunities. Ment. Health Phys. Act. 14, 136–139 (2018).
Kiang, M. V. et al. Sociodemographic characteristics of missing data in digital phenotyping. Sci. Rep. https://doi.org/10.1038/s41598-021-94516-7 (2021).
Dixon, W. G. et al. Charting a course for smartphones and wearables to transform population health research. J. Med. Internet Res. 25, e42449 (2023).
Barnett, I., Torous, J., Staples, P., Keshavan, M. & Onnela, J.-P. Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data. J. Am. Med. Inform. Assoc. 25, 1669–1674 (2018).
Benoit, J., Onyeaka, H., Keshavan, M. & Torous, J. Systematic review of digital phenotyping and machine learning in psychosis spectrum illnesses. Harvard Rev. Psychiatry https://doi.org/10.1097/HRP.0000000000000268 (2020).
De Angel, V. et al. Digital health tools for the passive monitoring of depression: a systematic review of methods. npj Digital Med. https://doi.org/10.1038/s41746-021-00548-8 (2022).
Gomes, E. et al. Effects of a group physical activity program on physical fitness and quality of life in individuals with schizophrenia. Ment. Health Phys. Act. 7, 155–162 (2014).
Torous, J. et al. Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia. NPJ Digital Med. 1, 15 (2018).
Afonso, P., Brissos, S., Figueira, M. L. & Paiva, T. Schizophrenia patients with predominantly positive symptoms have more disturbed sleep-wake cycles measured by actigraphy. Psychiatry Res. 189, 62–66 (2011).
Wulff, K., Dijk, D. J., Middleton, B., Foster, R. G. & Joyce, E. M. Sleep and circadian rhythm disruption in schizophrenia. Br. J. Psychiatry 200, 308–316 (2012).
Adler, D. A., Wang, F., Mohr, D. C. & Choudhury, T. Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies. PLoS ONE 17, e0266516 (2022).
Raugh, I. M. et al. Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia. J. Psychiatr. Res. 138, 436–443 (2021).
Narkhede, S. M. et al. Machine learning identifies digital phenotyping measures most relevant to negative symptoms in psychotic disorders: implications for Clinical Trials. Schizophr. Bull. 48, 425–436 (2022).
Raugh, I. M. et al. Geolocation as a digital phenotyping measure of negative symptoms and functional outcome. Schizophr. Bull. https://doi.org/10.1093/schbul/sbaa121 (2020).
Fang, S.-H. et al. Associations between sleep quality and inflammatory markers in patients with schizophrenia. Psychiatry Res. 246, 154–160 (2016).
Deenik, J. et al. Changes in physical and psychiatric health after a multidisciplinary lifestyle enhancing treatment for inpatients with severe mental illness: The MULTI study I. Schizophr. Res. 204, 360–367 (2019).
Deenik, J., Tenback, D. E., Tak, E. C. P. M., Hendriksen, I. J. M. & van Harten, P. N. Improved psychosocial functioning and quality of life in inpatients with severe mental illness receiving a multidisciplinary lifestyle enhancing treatment. The MULTI study II. Ment. Health Phys. Act. 15, 145–152 (2018).
Browne, J. et al. Targeting physical health in schizophrenia: results from the Physical Activity Can Enhance Life (PACE-Life) 24-week open trial. Ment. Health Phys. Act. 20, 100393 (2021).
Mow, J. L. et al. Smartphone-based mobility metrics capture daily social motivation and behavior in schizophrenia. Schizophr. Res. 250, 13–21 (2022).
Thonon, B., Levaux, M.-N., van Aubel, E. & Laroi, F. A group intervention for motivational deficits: preliminary investigation of a blended care approach using ambulatory assessment. Behav. Modif. 46, 1167–1197 (2022).
Jongs, N. et al. A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data. Transl. Psychiatry 10, 211 (2020).
Fowler, J. C. et al. Hummingbird study: results from an exploratory trial assessing the performance and acceptance of a digital medicine system in adults with schizophrenia, schizoaffective disorder, or first-episode psychosis. Neuropsychiatr. Dis. Treat. 17, 483–492 (2021).
Bueno-Antequera, J., Oviedo-Caro, M. A. & Munguia-Izquierdo, D. Ideal cardiovascular health and its association with sedentary behaviour and fitness in psychiatric patients. The PsychiActive project. Nutr., Metab. Cardiovasc. Dis. 28, 900–908 (2018).
Shamir, E. et al. Melatonin improves sleep quality of patients with chronic schizophrenia. J. Clin. Psychiatry 61, 373–377 (2000).
Depp, C. A. et al. GPS mobility as a digital biomarker of negative symptoms in schizophrenia: a case control study. npj Digital Med. 2, 108 (2019).
Henson, P., Pearson, J. F., Keshavan, M. & Torous, J. Impact of dynamic greenspace exposure on symptomatology in individuals with schizophrenia. PLoS ONE 15, e0238498 (2020).
Parrish, E. M. et al. Emotional determinants of life-space through GPS and ecological momentary assessment in schizophrenia: What gets people out of the house? Schizophr. Res. 224, 67–73 (2020).
Osipov, M., Behzadi, Y., Kane, J. M., Petrides, G. & Clifford, G. D. Objective identification and analysis of physiological and behavioral signs of schizophrenia. J. Ment. Health 24, 276–282 (2015).
Reinertsen, E. et al. Continuous assessment of schizophrenia using heart rate and accelerometer data. Physiol. Meas. 38, 1456–1471 (2017).
Reinertsen, E., Shashikumar, S. P., Shah, A. J., Nemati, S. & Clifford, G. D. Multiscale network dynamics between heart rate and locomotor activity are altered in schizophrenia. Physiol. Meas. 39, 115001 (2018).
Bengtsson, J., Olsson, E., Igelstrom, H., Persson, J. & Boden, R. Ambulatory heart rate variability in schizophrenia or depression: impact of anticholinergic burden and other factors. J. Clin. Psychopharmacol. 41, 121–128 (2021).
Strauss, G. P. et al. Validation of accelerometry as a digital phenotyping measure of negative symptoms in schizophrenia. Schizophrenia (Heidelb., Ger.) 8, 37 (2022).
Mayeli, A. et al. Shared and distinct abnormalities in sleep-wake patterns and their relationship with the negative symptoms of Schizophrenia Spectrum Disorder patients. Mol. Psychiatry https://doi.org/10.1038/s41380-023-02050-x (2023).
Dennison, C. A. et al. Association of genetic liability for psychiatric disorders with accelerometer-assessed physical activity in the UK Biobank. PLoS ONE 16, e0249189 (2021).
Ben-Zeev, D. et al. CrossCheck: integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse. Psychiatr. Rehabilit. J. 40, 266–275 (2017).
Buck, B. et al. Capturing behavioral indicators of persecutory ideation using mobile technology. J. Psychiatr. Res. 116, 112–117 (2019).
He-Yueya, J. et al. Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability. npj Schizophrenia 6, 35 (2020).
Barnett, I. et al. Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology 43, 1660–1666 (2018).
Adler, D. A. et al. Predicting early warning signs of psychotic relapse from passive sensing data: an approach using encoder-decoder neural networks. JMIR mHealth uHealth 8, e19962 (2020).
Browne, J. et al. Virtual group-based walking intervention for persons with schizophrenia: a pilot randomized controlled trial. Ment. Health Phys. Act. 24, 100515 (2023).
Diamond, R. et al. The physical activity profiles of patients with persecutory delusions. Ment. Health Phys. Act. 23, 100462 (2022).
von Kanel, S. et al. Measuring catatonia motor behavior with objective instrumentation. Front. Psychiatry 13, 880747 (2022).
Zarbo, C. et al. Ecological monitoring of physical activity, emotions and daily life activities in schizophrenia: the DiAPAson study. BMJ Mental Health https://doi.org/10.1136/bmjment-2023-300836 (2023).
Holt, R. I. G. et al. Structured lifestyle education for people with schizophrenia, schizoaffective disorder and first-episode psychosis (STEPWISE): randomised controlled trial. Br. J. Psychiatry 214, 63–73 (2019).
Troiano, R. P. et al. Physical activity in the United States measured by accelerometer. Med. Sci. Sports Exerc. 40, 181–188 (2008).
Choi, L., Liu, Z., Matthews, C. E. & Buchowski, M. S. Validation of accelerometer wear and nonwear time classification algorithm. Med. Sci. Sports Exerc. 43, 357–364 (2011).
Martanto, W. et al. Association between wrist wearable digital markers and clinical status in Schizophrenia. Gen. Hosp. Psychiatry 70, 134–136 (2021).
Cohen, A. et al. Relapse prediction in schizophrenia with smartphone digital phenotyping during COVID-19: a prospective, three-site, two-country, longitudinal study. Schizophrenia 9, 6 (2023).
Lakhtakia, T. et al. Smartphone digital phenotyping, surveys, and cognitive assessments for global mental health: Initial data and clinical correlations from an international first episode psychosis study. Digital Health 8, 1–18 (2022).
Henson, P., Barnett, I., Keshavan, M. & Torous, J. Towards clinically actionable digital phenotyping targets in schizophrenia. npj Schizophrenia 6, 13 (2020).
Barnett, I. & Onnela, J. P. Inferring mobility measures from GPS traces with missing data. Biostatistics 21, e98–e112 (2020).
Brønd, J. C., Andersen, L. B. & Arvidsson, D. Generating ActiGraph counts from raw acceleration recorded by an alternative monitor. Med. Sci. Sports Exerc. 49, 2351–2360 (2017).
Bai, J. et al. An activity index for raw accelerometry data and its comparison with other activity metrics. PLoS ONE 11, e0160644 (2016).
Menghini, L., Cellini, N., Goldstone, A., Baker, F. C. & De Zambotti, M. A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code. Sleep https://doi.org/10.1093/sleep/zsaa170 (2021).
Cole, R. J., Kripke, D. F., Gruen, W., Mullaney, D. J. & Gillin, J. C. Automatic sleep/wake identification from wrist activity. Sleep 15, 461–469 (1992).
Rahimi-Eichi, H. et al. Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): algorithm development and validation. JMIR mHealth uHealth 9, e29849 (2021).
Tan, Z.-H., Sarkar, A. K. & Dehak, N. rVAD: an unsupervised segment-based robust voice activity detection method. Comput. Speech Lang. 59, 1–21 (2020).
Migueles, J. H., Rowlands, A. V., Huber, F., Sabia, S. & Van Hees, V. T. GGIR: a research community–driven open source R package for generating physical activity and sleep outcomes from multi-day raw accelerometer data. J. Meas. Phys. Behav. 2, 188–196 (2019).
Blume, C., Santhi, N. & Schabus, M. ‘nparACT’ package for R: a free software tool for the non-parametric analysis of actigraphy data. MethodsX 3, 430–435 (2016).
Zlatintsi, A. et al. E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures. Sensors (Basel, Switzerland) https://doi.org/10.3390/s22197544 (2022).
Juda, M., Pater, J., Mistlberger, R. E. & Schutz, C. G. Sleep and rest-activity rhythms in recovering patients with severe concurrent mental and substance use disorder: a pilot study. J. Dual Diagn. 19, 26–39 (2023).
Bromundt, V. et al. Sleep – wake cycles and cognitive functioning in schizophrenia. Br. J. Psychiatry 198, 269–276 (2011).
Fasmer, E. E., Fasmer, O. B., Berle, J. O., Oedegaard, K. J. & Hauge, E. R. Graph theory applied to the analysis of motor activity in patients with schizophrenia and depression. PLoS ONE 13, e0194791 (2018).
Kas, M. J. H. et al. Digital behavioural signatures reveal trans-diagnostic clusters of Schizophrenia and Alzheimer’s disease patients: Trans-diagnostic clustering of digital biotypes. Eur. Neuropsychopharmacol. 78, 3–12 (2024).
Kalisperakis, E. et al. Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders. Front. Psychiatry 14, 1024965 (2023).
Skeldon, A. C., Dijk, D.-J., Meyer, N. & Wulff, K. Extracting circadian and sleep parameters from longitudinal data in schizophrenia for the design of pragmatic light interventions. Schizophr. Bull. 48, 447–456 (2022).
Kuula, L., Halonen, R., Lipsanen, J. & Pesonen, A.-K. Adolescent circadian patterns link with psychiatric problems: a multimodal approach. J. Psychiatr. Res. 150, 219–226 (2022).
Pieters, L. E., Deenik, J., Tenback, D. E., Oort, J. V. & Harten, P. N. V. Exploring the relationship between movement disorders and physical activity in patients with schizophrenia: an actigraphy study. Schizophr. Bull. 47, 906–914 (2021).
Liebenthal, E. et al. Linguistic and non-linguistic markers of disorganization in psychotic illness. Schizophr. Res. https://doi.org/10.1016/j.schres.2022.12.003 (2022).
Mandel, F., Ghosh, R. P. & Barnett, I. Neural networks for clustered and longitudinal data using mixed effects models. Biometrics https://doi.org/10.1111/biom.13615 (2021).
Kay, S. R., Fiszbein, A. & Opler, L. A. The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophr. Bull. 13, 261–276 (1987).
Cella, M. et al. Evaluating the mechanisms of social cognition intervention in schizophrenia: a proof-of-concept trial. Psychiatry Res. 319, 114963 (2022).
Orleans-Pobee, M. et al. Physical Activity Can Enhance Life (PACE-Life): results from a 10-week walking intervention for individuals with schizophrenia spectrum disorders. J. Ment. Health 31, 357–365 (2022).
Buck, B. et al. Relationships between smartphone social behavior and relapse in schizophrenia: a preliminary report. Schizophr. Res. 208, 167–172 (2019).
Walther, S., Ramseyer, F., Horn, H., Strik, W. & Tschacher, W. Less structured movement patterns predict severity of positive syndrome, excitement, and disorganization. Schizophr. Bull. 40, 585–591 (2014).
Lamichhane, B., Zhou, J. & Sano, A. Psychotic relapse prediction in schizophrenia patients using a personalized mobile sensing-based supervised deep learning model. IEEE J. Biomed. Health Inform. 10.1109/JBHI.2023.3265684 (2023).
Nguyen, D.-K., Chan, C.-L., Li, A.-H. A., Phan, D.-V. & Lan, C.-H. Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data. Health Inform. J. 28, 14604582221137537 (2022).
Price, G. D. et al. An unsupervised machine learning approach using passive movement data to understand depression and schizophrenia. J. Affect. Disord. 316, 132–139 (2022).
Lahti, A. C., Wang, D., Pei, H., Baker, S. & Narayan, V. A. Clinical utility of wearable sensors and patient-reported surveys in patients with schizophrenia: noninterventional, observational study. JMIR Ment. Health 8, e26234 (2021).
Rohani, D. A., Faurholt-Jepsen, M., Kessing, L. V. & Bardram, J. E. Correlations between objective behavioral features collected from mobile and wearable devices and depressive mood symptoms in patients with affective disorders: systematic review. JMIR Mhealth Uhealth 6, e165 (2018).
Bayoumy, K. et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat. Rev. Cardiol. 18, 581–599 (2021).
Stradford, L. et al. Wearable activity tracker study exploring rheumatoid arthritis patients’ disease activity using patient-reported outcome measures, clinical measures, and biometric sensor data (the wear study). Contemp. Clin. Trials Commun. 38, 101272 (2024).
Jakobsen, P. et al. in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). (IEEE).
Asgari Mehrabadi, M. et al. Sleep tracking of a commercially available smart ring and smartwatch against medical-grade actigraphy in everyday settings: instrument validation study. JMIR Mhealth Uhealth 8, e20465 (2020).
Allan, S. et al. Adverse events reporting in digital interventions evaluations for psychosis: a systematic literature search and individual level content analysis of adverse event reports. Schizophr. Bull. https://doi.org/10.1093/schbul/sbae031 (2024).
Eisner, E. et al. Measurement of adverse events in studies of digital health interventions for psychosis: guidance and recommendations based on a literature search and framework analysis of standard operating procedures. Schizophr. Bull. https://doi.org/10.1093/schbul/sbae048 (2024).
Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Br. Med. J. https://doi.org/10.1136/bmj.n71 (2021).
Steyerberg, E. W. et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. https://doi.org/10.1371/journal.pmed.1001381 (2013).
Riley, R. D. et al. Prognosis Research Strategy (PROGRESS) 2: prognostic factor research. PLoS Med. 10, e1001380–e1001380 (2013).
Hayden, J. A., van der Windt, D. A., Cartwright, J. L., Côté, P. & Bombardier, C. Assessing bias in studies of prognostic factors. Ann. Intern. Med. 158, 280–286 (2013).
Wolff, R. F. et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann. Intern. Med. 170, 51–58 (2019).
Savage, C. L. G., Orth, R. D., Jacome, A. M., Bennett, M. E. & Blanchard, J. J. Assessing the psychometric properties of the PROMIS sleep measures in persons with psychosis. Sleep https://doi.org/10.1093/sleep/zsab140 (2021).
Reeve, S., Sheaves, B. & Freeman, D. Sleep disorders in early psychosis: incidence, severity, and association with clinical symptoms. Schizophr. Bull. 45, 287–295 (2019).
Baandrup, L. & Jennum, P. J. A validation of wrist actigraphy against polysomnography in patients with schizophrenia or bipolar disorder. Neuropsychiatr. Dis. Treat. 11, 2271–2277 (2015).
Walther, S., Horn, H., Koschorke, P., Muller, T. J. & Strik, W. Increased motor activity in cycloid psychosis compared to schizophrenia. World J. Biol. Psychiatry 10, 746–751 (2009).
Walther, S., Koschorke, P., Horn, H. & Strik, W. Objectively measured motor activity in schizophrenia challenges the validity of expert ratings. Psychiatry Res. 169, 187–190 (2009).
Walther, S. et al. Higher motor activity in schizophrenia patients treated with olanzapine versus risperidone. J. Clin. Psychopharmacol. 30, 181–184 (2010).
Wichniak, A. et al. Actigraphic monitoring of activity and rest in schizophrenic patients treated with olanzapine or risperidone. J. Psychiatr. Res. 45, 1381–1386 (2011).
Wang, J. et al. Both physical activity and food intake are associated with metabolic risks in patients with schizophrenia. Schizophr. Res. 140, 260–261 (2012).
Abel, D. B., Salyers, M. P., Wu, W., Monette, M. A. & Minor, K. S. Quality versus quantity: determining real-world social functioning deficits in schizophrenia. Psychiatry Res. 301, 113980 (2021).
Abel, D. B. & Minor, K. S. Social functioning in schizophrenia: comparing laboratory-based assessment with real-world measures. J. Psychiatr. Res. 138, 500–506 (2021).
Abplanalp, S. J. et al. Feasibility of using smartphones to capture speech during social interactions in schizophrenia. Schizophr. Res. 228, 51–52 (2021).
Reinertsen, E. & Clifford, G. D. A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiol. Meas. 39, 05TR01 (2018).
Wainberg, M. et al. Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: a cross-sectional study of 89,205 participants from the UK Biobank. PLoS Med. 18, e1003782 (2021).
Firth, J. et al. The validity and value of self-reported physical activity and accelerometry in people with schizophrenia: a population-scale study of the UK biobank. Schizophr. Bull. 44, 1293–1300 (2018).
Deenik, J. et al. Physical activity and quality of life in long-term hospitalized patients with severe mental illness: A cross-sectional study. BMC Psychiatry 17, 298 (2017).
Smit, M. M. C., Waal, E. D., Tenback, D. E. & Deenik, J. Evaluating the implementation of a multidisciplinary lifestyle intervention for people with severe mental illness in sheltered housing: effectiveness-implementation hybrid randomised controlled trial. BJPsych Open 8, e201 (2022).
Kruisdijk, F. et al. Accelerometer-measured sedentary behaviour and physical activity of inpatients with severe mental illness. Psychiatry Res. 254, 67–74 (2017).
Gomes, E. et al. Quality of life and physical activity levels in outpatients with schizophrenia. Rev. Bras. Psiquiatr. 38, 157–160 (2016).
Andersen, E. et al. Physical activity pattern and cardiorespiratory fitness in individuals with schizophrenia compared with a population-based sample. Schizophr. Res. 201, 98–104 (2018).
Andersen, E. et al. Effect of high-intensity interval training on cardiorespiratory fitness, physical activity and body composition in people with schizophrenia: a randomized controlled trial. BMC Psychiatry 20, 425 (2020).
Engh, J. A. et al. Objectively assessed daily steps-not light intensity physical activity, moderate-to-vigorous physical activity and sedentary time-is associated with cardiorespiratory fitness in patients with schizophrenia. Front. Psychiatry 10, 82 (2019).
Holmen, T. L. et al. The association between cardiorespiratory fitness and cognition appears neither related to current physical activity nor mediated by brain-derived neurotrophic factor in a sample of outpatients with schizophrenia. Front. Psychiatry 10, 785 (2019).
Janney, C. A. et al. Sedentary behavior and psychiatric symptoms in overweight and obese adults with schizophrenia and schizoaffective disorders (WAIST Study). Schizophr. Res. 145, 63–68 (2013).
Janney, C. A. et al. Physical activity and sedentary behavior measured objectively and subjectively in overweight and obese adults with schizophrenia or schizoaffective disorders. J. Clin. Psychiatry 76, e1277–e1284 (2015).
Lindamer, L. A. et al. Assessment of physical activity in middle-aged and older adults with schizophrenia. Schizophr. Res. 104, 294–301 (2008).
Duncan, M. J., Arbour-Nicitopoulos, K., Subramaniapillai, M., Remington, G. & Faulkner, G. Revisiting the International Physical Activity Questionnaire (IPAQ): assessing sitting time among individuals with schizophrenia. Psychiatry Res. 271, 311–318 (2019).
Berry, A., Drake, R. J., Butcher, I. & Yung, A. R. Examining the feasibility, acceptability, validity and reliability of physical activity, sedentary behaviour and sleep measures in people with schizophrenia. Ment. Health Phys. Act. 21, 100415 (2021).
Brobakken, M. F. et al. A comprehensive cardiovascular disease risk profile in patients with schizophrenia. Scand. J. Med. Sci. Sports 29, 575–585 (2019).
Jerome, G. J. et al. Physical activity levels of persons with mental illness attending psychiatric rehabilitation programs. Schizophr. Res. 108, 252–257 (2009).
Duncan, M. J., Arbour-Nicitopoulos, K., Subramanieapillai, M., Remington, G. & Faulkner, G. Revisiting the International Physical Activity Questionnaire (IPAQ): Assessing physical activity among individuals with schizophrenia. Schizophr. Res. 179, 2–7 (2017).
Gorczynski, P., Faulkner, G., Cohn, T. & Remington, G. Examining the efficacy and feasibility of exercise counseling in individuals with schizophrenia: a single-case experimental study. Ment. Health Phys. Act. 7, 191–197 (2014).
Gorczynski, P., Faulkner, G., Cohn, T. & Remington, G. Examining strategies to improve accelerometer compliance for individuals living with schizophrenia. Psychiatr. Rehabilit. J. 37, 333–335 (2014).
Grassmann, V., Subramaniapillai, M., Duncan, M., Arbour-Nicitopoulos, K. & Faulkner, G. E. The relationship between moderate-to-vigorous physical activity and executive function among individuals with schizophrenia: differences by illness duration. Rev. Bras. Psiquiatr. 39, 309–315 (2017).
Oliva, V. et al. Patterns of antipsychotic prescription and accelerometer-based physical activity levels in people with schizophrenia spectrum disorders: a multicenter, prospective study. Int. Clin. Psychopharmacol. 38, 28–39 (2023).
Chen, L. J., Steptoe, A., Chung, M. S. & Ku, P. W. Association between actigraphy-derived physical activity and cognitive performance in patients with schizophrenia. Psychol. Med. 46, 2375–2384 (2016).
Faulkner, G., Cohn, T. & Remington, G. Validation of a physical activity assessment tool for individuals with schizophrenia. Schizophr. Res. 82, 225–231 (2006).
Bueno-Antequera, J., Oviedo-Caro, M. & Munguia-Izquierdo, D. Sedentary behaviour, physical activity, cardiorespiratory fitness and cardiometabolic risk in psychosis: the PsychiActive project. Schizophr. Res. 195, 142–148 (2018).
Afonso, P., Figueira, M. L. & Paiva, T. Sleep-wake patterns in schizophrenia patients compared to healthy controls. World J. Biol. Psychiatry 15, 517–524 (2014).
Walther, S. et al. Quantitative motor activity differentiates schizophrenia subtypes. Neuropsychobiology 60, 80–86 (2009).
Beebe, L. H. et al. A pilot study describing physical activity in persons with schizophrenia spectrum disorders (Ssds) after an exercise program. Issues Ment. Health Nurs. 34, 214–219 (2013).
Williams, J. et al. ‘Walk this way’: results from a pilot randomised controlled trial of a health coaching intervention to reduce sedentary behaviour and increase physical activity in people with serious mental illness. BMC Psychiatry 19, 287 (2019).
Scheewe, T. W. et al. Low physical activity and cardiorespiratory fitness in people with schizophrenia: a comparison with matched healthy controls and associations with mental and physical health. Front. Psychiatry 10, 87 (2019).
Vancampfort, D. et al. Lower cardiorespiratory fitness is associated with more time spent sedentary in first episode psychosis: a pilot study. Psychiatry Res. 253, 13–17 (2017).
Vancampfort, D. et al. Validity and correlates of the International Physical Activity Questionnaire in first-episode psychosis. Early Interv. Psychiatry 13, 562–567 (2019).
Cella, M. et al. Using wearable technology to detect the autonomic signature of illness severity in schizophrenia. Schizophr. Res. 195, 537–542 (2018).
Passive data collection methods are increasingly being used in the field of mental health to remotely monitor symptoms and behaviors of individuals with psychosis and schizophrenia. In this post, we will conduct a systematic review of the current literature on the use of passive data for remote monitoring in these populations.Passive data collection refers to the continuous and unobtrusive monitoring of individuals using sensors and other technology to gather information about their daily activities, movements, and interactions. This data can provide valuable insights into the progression of symptoms, medication adherence, and overall well-being of individuals with psychosis and schizophrenia.
Several studies have explored the use of passive data collection methods such as smartphone sensors, wearable devices, and smart home technology to monitor symptoms and behaviors in individuals with psychosis and schizophrenia. These studies have shown promising results in terms of early detection of relapse, predicting hospitalizations, and improving outcomes through personalized interventions.
However, challenges remain in terms of privacy concerns, data security, and the integration of passive data into existing clinical workflows. Additional research is needed to further validate the effectiveness of passive data for remote monitoring in psychosis and schizophrenia, as well as to explore the potential barriers and facilitators to its implementation in clinical practice.
Overall, the use of passive data for remote monitoring in psychosis and schizophrenia holds great promise for improving the care and outcomes of individuals with these conditions. By conducting a systematic review of the current literature, we can better understand the current state of research in this area and identify key areas for future investigation and implementation.
Tags:
- Passive data monitoring
- Remote monitoring in psychosis
- Schizophrenia research
- Systematic review of passive data
- Mental health monitoring
- Remote sensing technology
- Psychosis and schizophrenia studies
- Data collection in mental health
- Wearable technology in mental health
- Remote patient monitoring
#systematic #review #passive #data #remote #monitoring #psychosis #schizophrenia
Machine Learning and Knowledge-Based Systems: Systematic Introduction with P
Machine Learning and Knowledge-Based Systems: Systematic Introduction with P
Price : 21.03
Ends on : N/A
View on eBay
Machine Learning and Knowledge-Based Systems: Systematic Introduction with PythonIn today’s digital age, machine learning and knowledge-based systems have become essential tools for businesses and organizations looking to gain insights from their data. These systems use algorithms and statistical models to analyze large amounts of data and make predictions or decisions based on that analysis.
Python has become the go-to programming language for building machine learning and knowledge-based systems due to its simplicity, flexibility, and extensive library support. In this post, we will provide a systematic introduction to machine learning and knowledge-based systems using Python.
First, we will cover the basics of machine learning, including supervised and unsupervised learning, regression, classification, and clustering. We will then delve into knowledge-based systems, which use expert knowledge to make decisions or provide recommendations.
Next, we will introduce popular Python libraries such as scikit-learn, TensorFlow, and PyTorch, which provide a wide range of tools for building machine learning and knowledge-based systems. We will walk through examples of using these libraries to train models, evaluate their performance, and make predictions.
Finally, we will discuss best practices for building and deploying machine learning and knowledge-based systems in real-world applications. This includes data preprocessing, feature engineering, model selection, and evaluation.
By the end of this systematic introduction, you will have a solid understanding of machine learning and knowledge-based systems and be able to start building your own systems using Python. So, grab your favorite IDE and let’s get started on this exciting journey into the world of intelligent systems!
#Machine #Learning #KnowledgeBased #Systems #Systematic #Introduction,machine learning: an applied mathematics introductionNeurosyphilis, Modern Systematic Diagnosis and Treatment (Classic Reprint)
Neurosyphilis, Modern Systematic Diagnosis and Treatment (Classic Reprint)
Price : 25.80
Ends on : N/A
View on eBay
Neurosyphilis, Modern Systematic Diagnosis and Treatment (Classic Reprint)Neurosyphilis is a serious infection of the nervous system caused by the bacteria Treponema pallidum, the same bacteria that causes syphilis. This condition can lead to a variety of neurological symptoms, including confusion, memory loss, and even paralysis if left untreated.
In this classic reprint, we delve into the modern systematic diagnosis and treatment of neurosyphilis. From the initial evaluation and testing to the latest treatment options, this comprehensive guide provides valuable insights for healthcare professionals and patients alike.
With advancements in medical technology and understanding of this condition, early detection and prompt treatment are crucial in preventing long-term complications. This reprint serves as a timeless resource for anyone seeking to learn more about neurosyphilis and its management.
Don’t miss out on this invaluable resource for understanding and addressing neurosyphilis. Order your copy of “Neurosyphilis, Modern Systematic Diagnosis and Treatment” today.
#Neurosyphilis #Modern #Systematic #Diagnosis #Treatment #Classic #ReprintSystematic Fieldwork: Ethnographic Analysis and Data Management
Systematic Fieldwork: Ethnographic Analysis and Data Management
Price : 21.86
Ends on : N/A
View on eBay
Systematic Fieldwork: Ethnographic Analysis and Data ManagementEthnographic research involves immersing oneself in a particular culture or community to gain a deep understanding of their beliefs, practices, and social norms. This type of research requires systematic fieldwork, where researchers spend extended periods of time in the field collecting data through observations, interviews, and interactions with members of the community.
One key aspect of systematic fieldwork is the rigorous analysis of data collected during the research process. Ethnographers must carefully document their observations, interviews, and any other data collected, and then analyze this information to identify patterns, themes, and insights that can help them better understand the culture they are studying.
Data management is another crucial component of systematic fieldwork. Ethnographers must carefully organize and store their data to ensure that it is easily accessible and can be used to draw meaningful conclusions. This may involve creating detailed field notes, transcribing interviews, and using software tools to organize and analyze data.
Overall, systematic fieldwork is essential for conducting rigorous ethnographic research. By carefully collecting, analyzing, and managing data, researchers can gain valuable insights into the cultures and communities they are studying, and contribute to a deeper understanding of human behavior and social dynamics.
#Systematic #Fieldwork #Ethnographic #Analysis #Data #Management, Data Management
Systematic Introduction to Expert Systems : Knowledge Representations and Pro…
Systematic Introduction to Expert Systems : Knowledge Representations and Pro…
Price : 136.23
Ends on : N/A
View on eBay
Expert systems are a type of artificial intelligence that mimic the decision-making abilities of a human expert in a specific domain. In order to function effectively, expert systems rely on a structured knowledge representation and reasoning process. In this post, we will provide a systematic introduction to expert systems, focusing on knowledge representations and problem-solving techniques.Knowledge representations in expert systems play a crucial role in capturing and organizing the knowledge of human experts. There are several common knowledge representation techniques used in expert systems, including rules, frames, semantic networks, and production systems. Rules are a set of conditional statements that guide the reasoning process of the expert system. Frames are a way to represent knowledge in a structured format, with slots and values that define the properties of an object. Semantic networks represent knowledge as a network of nodes and links, with each node representing a concept and each link representing a relationship between concepts. Production systems are a set of rules that guide the reasoning process of the expert system, with each rule triggering a specific action based on the current state of the system.
In addition to knowledge representations, expert systems also rely on problem-solving techniques to make decisions and provide recommendations. One common problem-solving technique used in expert systems is inference, which involves applying logical rules to derive new knowledge from existing knowledge. Inference can be performed using forward chaining, where the system starts with the available facts and applies rules to derive new conclusions, or backward chaining, where the system starts with the desired goal and works backwards to determine the steps needed to achieve that goal.
Overall, expert systems are a valuable tool for organizations looking to leverage the knowledge of their experts and improve decision-making processes. By utilizing structured knowledge representations and problem-solving techniques, expert systems can provide accurate and reliable recommendations in a variety of domains. Stay tuned for our next post, where we will delve deeper into the implementation and evaluation of expert systems.
#Systematic #Introduction #Expert #Systems #Knowledge #Representations #Pro..
Systematic Instruction for Students with Moderate and Severe Disabilities – GOOD
Systematic Instruction for Students with Moderate and Severe Disabilities – GOOD
Price : 7.55
Ends on : N/A
View on eBay
Systematic Instruction: Empowering Students with Moderate and Severe DisabilitiesIn the world of education, it is crucial to provide systematic instruction to students with moderate and severe disabilities. These students often face unique challenges and require specialized support to reach their full potential. By implementing a systematic approach to instruction, educators can create a more supportive and inclusive learning environment for these students.
One key component of systematic instruction is breaking down complex tasks into smaller, manageable steps. This approach helps students build on their existing skills and gradually progress towards more challenging goals. By providing clear and structured instruction, educators can help students with disabilities develop a greater sense of independence and confidence in their abilities.
Another important aspect of systematic instruction is providing consistent feedback and reinforcement. Positive reinforcement can help motivate students and reinforce their learning progress. By consistently praising and rewarding students for their efforts, educators can help build their self-esteem and encourage them to continue working towards their goals.
Additionally, systematic instruction involves individualizing instruction to meet the unique needs of each student. Educators must take into account the strengths, weaknesses, and learning styles of their students to create personalized instruction plans. By tailoring instruction to the specific needs of each student, educators can help maximize their learning potential and promote academic success.
Overall, systematic instruction plays a crucial role in supporting students with moderate and severe disabilities. By providing clear and structured instruction, consistent feedback and reinforcement, and personalized instruction plans, educators can help empower these students to reach their full potential. Together, we can create a more inclusive and supportive learning environment for all students, regardless of their abilities.
#Systematic #Instruction #Students #Moderate #Severe #Disabilities #GOOD,students and
professionals![Data Mining 101 [Systematic Review]: A Holistic View of Data Mining: Covering Its Theoretical Foundations and Practical Real-World Applications](https://ziontechgroup.com/wp-content/uploads/2024/12/61adqifguVL._SL1500_.jpg)
Data Mining 101 [Systematic Review]: A Holistic View of Data Mining: Covering Its Theoretical Foundations and Practical Real-World Applications
Price: $2.99
(as of Dec 24,2024 11:45:04 UTC – Details)
ASIN : B0DFQGL3N1
Publication date : August 29, 2024
Language : English
File size : 645 KB
Simultaneous device usage : Unlimited
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 22 pages
Data Mining 101 [Systematic Review]: A Holistic View of Data Mining: Covering Its Theoretical Foundations and Practical Real-World ApplicationsData mining is a powerful tool used to extract meaningful insights from large datasets. It involves the process of discovering patterns, trends, and relationships within data to help businesses make informed decisions. In this systematic review, we will provide a comprehensive overview of data mining, covering its theoretical foundations and practical real-world applications.
Theoretical Foundations of Data Mining
Data mining is rooted in the fields of statistics, machine learning, and database systems. It utilizes a variety of algorithms and techniques to analyze data and uncover valuable information. Some of the key theoretical foundations of data mining include:
1. Statistics: Data mining often relies on statistical methods to analyze data and make inferences. Techniques such as regression analysis, hypothesis testing, and clustering are commonly used in data mining.
2. Machine Learning: Machine learning algorithms play a crucial role in data mining, as they enable computers to learn from data and make predictions or decisions without being explicitly programmed. Supervised learning, unsupervised learning, and reinforcement learning are common machine learning approaches used in data mining.
3. Database Systems: Data mining requires access to large datasets stored in databases. Knowledge of database systems and query languages is essential for extracting and manipulating data for analysis.
Practical Real-World Applications of Data Mining
Data mining has a wide range of practical applications across various industries. Some of the common real-world applications of data mining include:
1. Customer Segmentation: Data mining is used to segment customers based on their behavior, preferences, and demographics. This helps businesses tailor their marketing strategies and product offerings to different customer segments.
2. Fraud Detection: Financial institutions use data mining to detect fraudulent transactions and activities. By analyzing patterns and anomalies in transaction data, data mining algorithms can identify potential fraud cases.
3. Predictive Maintenance: In manufacturing industries, data mining is used for predictive maintenance of equipment and machinery. By analyzing sensor data and historical maintenance records, data mining algorithms can predict when equipment is likely to fail and schedule maintenance proactively.
4. Healthcare Analytics: Data mining is used in healthcare to analyze patient data and improve diagnosis, treatment, and outcomes. By analyzing electronic health records and medical imaging data, data mining can help healthcare providers make more accurate and timely decisions.
In conclusion, data mining is a valuable tool for extracting insights from data and making informed decisions. By understanding its theoretical foundations and practical applications, businesses can leverage data mining to gain a competitive advantage and drive innovation.
#Data #Mining #Systematic #Review #Holistic #View #Data #Mining #Covering #Theoretical #Foundations #Practical #RealWorld #Applications
Neural Networks: A Systematic Introduction
Price:$99.99– $80.66
(as of Dec 24,2024 09:57:35 UTC – Details)
Publisher : Springer; 1st edition (July 12, 1996)
Language : English
Paperback : 522 pages
ISBN-10 : 3540605053
ISBN-13 : 978-3540605058
Item Weight : 1.55 pounds
Dimensions : 6.1 x 1.19 x 9.25 inches
Neural Networks: A Systematic IntroductionNeural networks have become an integral part of modern technology, powering everything from autonomous vehicles to virtual assistants. But what exactly are neural networks, and how do they work? In this post, we will provide a systematic introduction to neural networks, breaking down the complex concepts into easy-to-understand terms.
What are Neural Networks?
Neural networks are a type of artificial intelligence that is inspired by the structure of the human brain. They consist of interconnected nodes, or “neurons,” that work together to process and analyze data. These networks are capable of learning from data and making predictions or decisions based on that data.
How do Neural Networks Work?
At the core of a neural network is the neuron, which takes in input data, processes it using a set of weights and biases, and produces an output. These neurons are organized into layers, with each layer performing a specific task in the overall computation.
The first layer of a neural network is the input layer, which receives the initial data. The data is then passed through one or more hidden layers, where the neurons perform complex computations on the data. Finally, the output layer produces the final result of the neural network’s computation.
Training a Neural Network
One of the key features of neural networks is their ability to learn from data. This process, known as training, involves adjusting the weights and biases of the neurons in the network to minimize the error between the predicted output and the actual output.
During training, the network is fed a set of labeled data, with the desired output for each input. The network then adjusts its weights and biases using a process called backpropagation, which involves calculating the gradient of the error function and updating the weights in the opposite direction of the gradient.
Applications of Neural Networks
Neural networks have a wide range of applications in fields such as image and speech recognition, natural language processing, and financial forecasting. They are also used in autonomous systems, such as self-driving cars and drones, where they can make decisions based on real-time data.
In conclusion, neural networks are a powerful tool for processing and analyzing data, with the ability to learn and adapt to new information. By understanding the basic principles of neural networks, we can unlock their full potential in solving complex problems and advancing technology.
#Neural #Networks #Systematic #Introduction
Critical Thinking, Logic & Problem Solving: The Complete Guide to Superior Thinking, Systematic Problem Solving, Making Outstanding Decisions, and Uncover Logical Fallacies Like a Pro
Price:$27.75– $24.98
(as of Dec 24,2024 05:30:11 UTC – Details)
Customers say
Customers find the book’s content easy to understand and practical. They describe it as fantastic, great for the LSAT, and essential reading for everyone. The diagrams, charts, and graphs are appreciated by readers. However, opinions differ on the storytelling style – some find it extraordinary and masterful, while others feel the examples are too basic and lack effort to apply concepts to contemporary issues.
AI-generated from the text of customer reviews
Critical Thinking, Logic & Problem Solving: The Complete Guide to Superior Thinking, Systematic Problem Solving, Making Outstanding Decisions, and Uncover Logical Fallacies Like a ProIn a world filled with information overload and conflicting opinions, it has become more important than ever to develop strong critical thinking skills, sound logic, and effective problem-solving abilities. Whether you’re a student, a professional, or just someone looking to improve your decision-making process, mastering these skills is essential for success in today’s complex world.
This comprehensive guide will take you through the fundamentals of critical thinking, logic, and problem-solving, providing you with the tools and techniques needed to think more clearly, make better decisions, and avoid common pitfalls and logical fallacies.
From understanding the basic principles of critical thinking to applying them in real-life scenarios, this guide will help you develop a systematic approach to problem-solving that will set you apart from the rest. You’ll learn how to analyze arguments, evaluate evidence, spot logical fallacies, and make sound decisions based on reason and evidence.
Whether you’re facing a difficult decision, trying to solve a complex problem, or simply looking to enhance your cognitive abilities, this guide will equip you with the skills and knowledge you need to think more critically, reason more effectively, and solve problems with confidence. So don’t wait any longer – dive into the world of critical thinking, logic, and problem-solving and unlock your full potential today!
#Critical #Thinking #Logic #Problem #Solving #Complete #Guide #Superior #Thinking #Systematic #Problem #Solving #Making #Outstanding #Decisions #Uncover #Logical #Fallacies #Pro
