Adherence to Follow-Up in First-Episode Psychosis
Bibliographic record
Abstract
OBJECTIVE: To determine whether visible minority patients with first-episode psychosis are at higher risk for treatment nonadherence than white patients and elicit the perceptions of case managers regarding visible minority patients. METHODS: Data for 168 patients referred to a tertiary first-episode psychosis clinic from January 2008 to January 2012 were collected via chart review. For 110 patients, a questionnaire filled out by each patient's case manager collected quantitative and qualitative data regarding the case managers' perceptions of patients' insight, cooperation, and adherence to appointments and medication. Differential treatment adherence in white and visible minority patients was tested via χ² analyses. Case manager ratings of adherence were compared to objective data via Cohen κ. Qualitative data were analyzed via thematic analysis. RESULTS: Black patients had poorer follow-up compared to other patients (adjusted χ²₁ = 4.3, P = .04). Concordance of case manager-reported adherence and chart data was significant for the visible minority group only (κ = 0.4, P = .002). In case manager perceptions, there was no significant difference between ethnic groups in adherence to appointments and medication, insight, or family involvement. CONCLUSIONS: Although Canada is often perceived as tolerant of diversity, our data regarding poor follow-up in black patients indicate similar problems to those reported in the United Kingdom and United States. Clinicians may have low expectations for visible minority patients and thus notice more consistently when these patients adhere to treatment. This is the first study to examine ethnic differences in adherence to first-episode psychosis follow-up in a Canadian setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".