Adherence to follow-up in first-episode psychosis: ethnicity factors and case manager perceptions
Bibliographic record
Abstract
Objective: There are disparities in mental health care access and adherence for visible minority (VM) patients. The objectives of this study were: 1) to determine whether VM patients with first-episode psychosis (FEP) are at higher risk for treatment non-adherence than White patients, and 2) to elicit the perceptions of case managers (CMs) regarding VM patients. Method: Data regarding 168 patients referred to a First-Episode Psychosis Clinic from 2008 to 2011 were collected via chart review. For 110 patients, a questionnaire filled by each patient's CM collected quantitative and qualitative data regarding the CMs' perceptions of patients' insight, cooperation, and adherence to appointments and medication. Differential treatment adherence in White and VM patients were tested via chi-square analyses. CM ratings of adherence were compared to objective data via Cohen's kappa. Qualitative data was analyzed via thematic analysis. Results: Black patients had poorer follow-up compared to other patients (Ï2=5.4, df=1, p=0.02). Concordance of CM-reported adherence and chart data was significant for the VM group only (kappa= 0.444, p=0.002). In CM 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 UK and US. Clinicians may have low expectations for VM patients and therefore notice more consistently when VM patients adhere well to treatment. This is the first study to examine ethnic differences in adherence to FEP follow-up in a Canadian setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".