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Record W2802427014

Epidemiological and experimental evidence to improve antipsychotic medication adherence among patients with schizophrenia who are homeless and involved with the criminal justice system

2017· dissertation· en· W2802427014 on OpenAlexaboutno aff
Stefanie N. Rezansoff

Bibliographic record

VenueSummit (Simon Fraser University) · 2017
Typedissertation
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)EpidemiologyAntipsychoticPsychiatryCriminal justiceMedicinePsychologyClinical psychologyCriminologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Background: Schizophrenia consistently ranks among the leading causes of disability worldwide, and is significantly overrepresented in socially disadvantaged populations. Despite demonstrated efficacy of antipsychotic medication in research studies, poor adherence limits its effectiveness in real-world practice. Remarkably, antipsychotic adherence has never been examined in homeless or justice-involved patient cohorts under naturalistic conditions, and current treatment guidelines provide little information on practices to improve outcomes in these important subgroups. The three original research studies that comprise this thesis address this substantial omission in existing literature. Methods: The studies include population-level analysis, retrospective cohort design and a randomized controlled trial. Offenders diagnosed with schizophrenia, prescribed antipsychotic medication and convicted under British Columbia jurisdiction were the basis for longitudinal epidemiological analysis. A homeless cohort of Vancouver patients with severe mental illness enabled retrospective analysis of antipsychotic use, and examination of changes in adherence following randomization to different supported-housing treatment conditions. All three analyses drew on a centralized administrative repository of comprehensive prescription details. Adherence was operationalized using the medication possession ratio (MPR). Results: Over an average follow-up of 10 years, findings from the offender sample (n=11,462) revealed a mean MPR of 0.41. Results further demonstrated that patients who met guideline-level adherence (MPR≥0.80) were significantly less likely to be convicted of both violent and non-violent offences. 15-year retrospective analyses of homeless patients also showed an average MPR of 0.41. Higher antipsychotic adherence was significantly associated with duration of homelessness, prescription of long-acting injectable medication and primary care engagement. Randomization to market housing with assertive community treatment resulted in near guideline-level adherence (0.78), while assignment to congregate supported-housing and treatment as usual led to relatively low levels of adherence (0.55 and 0.61, respectively). Conclusion: Results demonstrate that homeless and/or justice-involved patients with schizophrenia have very low levels of adherence to prescribed antipsychotic medication. Findings were corroborated using two separate samples, in the context of universal health care, where prescribed medication is provided at no cost to patients of limited means. Action is needed to implement measures including those detailed in this research that have demonstrated promise to improve adherence among highly vulnerable patient groups.

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)0.007
metaresearch head score (Gemma)0.041
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 score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.027
GPT teacher head0.281
Teacher spread0.254 · 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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