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Record W2599919424 · doi:10.1093/schbul/sbx024.058

SU60. Risk and Protective Factors Associated With Psychotic Symptom Profiles of Marginally Housed Adults

2017· article· en· W2599919424 on OpenAlexaff
Andrea A. Jones, Kristina M. Gicas, Ric M. Procyshyn, Geoff Smith, Fidel Vila‐Rodriguez, Olga Leonova, Verena Langheimer, Emma Mitchell, Arün Dhir, Taylor S. Willi, Toby Schmitt, Donna J. Lang, Alasdair M. Barr, Tari Buchanan, William MacEwan, William J. Panenka, Allen E. Thornton, William G. Honer

Bibliographic record

VenueSchizophrenia Bulletin · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsPositive and Negative Syndrome ScalePsychologyPsychosisCannabisPsychiatrySchizophrenia (object-oriented programming)Cluster (spacecraft)Clinical psychologyObservational studyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Background: This study examined the characteristics and associated risk and protective factors of distinct psychotic symptom profiles exhibited by marginally housed adults. Methods: The Hotel Study is a longitudinal observational study of adults living in marginalized housing. Five psychosis symptoms (delusions, conceptual disorganization, hallucinations, suspiciousness, and unusual thought content) from the Positive and Negative Syndrome Scale (PANSS) were assessed monthly. Sociodemographic, psychiatric, medical, developmental, social, and substance use factors were also assessed. Two-step cluster analysis was employed to identify groups of people that shared similar symptom profiles at the time of their maximum (MaxTime) and minimum (MinTime) total symptom severity (PANSS) in 1 year. Multinomial logistic regression analysis was used to estimate the associations between factors and cluster membership. Paired Wilcoxon and McNemar’s tests were used to compare substance use at MaxTime and MinTime within each cluster. Results: In the first year of study, 404 participants had at least three 5-item PANSS assessments. Cluster analysis of the PANSS scores identified 3 clusters at MaxTime. The Severe Cluster (n = 74) endorsed severe psychosis symptoms, while the Variable Cluster (n = 147) endorsed supra-threshold delusions and hallucinations only. The Low Cluster (n = 183) did not endorse psychosis at MaxTime. Variable Cluster membership was associated with methamphetamine (OR, 95% CI: 2.28, 1.26–4.10) and cannabis use in the past week (OR, 95% CI: 2.69, 1.39–5.24), a history of traumatic brain injury (OR, 95% CI: 3.14, 1.41–7.01), and low social support at study entry (OR, 95% CI: 0.79, 0.63–0.99). The Variable Cluster experienced frequent transitions between psychotic and nonpsychotic states (median, IQR: 3, 1–5; P < .001), possibly exacerbated by methamphetamine (X2 = 6.86; P = .009) and alcohol use (X2 = 7.90; P = .005). Severe Cluster membership was associated with antipsychotic treatment (OR, 95% CI: 7.34, 3.21–16.81), methamphetamine (OR, 95% CI: 2.84, 1.36–5.90) and cannabis use in the past week (OR, 95% CI: 2.69, 1.39–5.24), and low social support at study entry (OR, 95% CI: 0.73, 0.53–0.99). The Severe Cluster had high rates of primary psychosis diagnosis (P < .001) and poorer psychosocial functioning (P < .001) than the Low Cluster. Symptoms may be exacerbated by recent cannabis (X2 = 5.06; P = .024), opioid (X2 = 4.00; P = .046), or alcohol use (X2 = 4.65; P = .031), but may be unaffected by methamphetamine (X2 = 0.44; P = .505) and antipsychotic use (X2 = 0.00; P = 1.000) in the Severe Cluster. Conclusion: A subset of marginally housed adults living with complex multimorbid illness experience severe psychotic symptoms that may be unresponsive to both antipsychotic treatment and methamphetamine use. These individuals may need alternative or additional forms of mental health care and rehabilitation support.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.335
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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