Engaging with care in an early intervention for psychosis program: The role of language, communication, and culture
Bibliographic record
Abstract
Language is an important aspect of communication and language status is known to impact healthcare accessibility, its perceived suitability, and outcomes. However, its influence on treatment engagement and/or disengagement is unknown. Our study therefore sought to investigate the impact of language on service disengagement in an early intervention psychosis program in Montreal, Quebec (a province with French as the official language). We aimed to compare service disengagement between a linguistic minority group (i.e., English) vis-à-vis those whose preferred language was French and to explore the role of language in service engagement. Using a mixed methods sequential design, we tested preferred language and several sociodemographic characteristics associated with service disengagement in a time-to-event analysis with Cox proportional hazards regression models ( N = 338). We then conducted two focus groups with English (seven patients) and French speakers (five patients) to further explore differences between the two linguistic groups. Overall, 24% ( n = 82) disengaged from the service before the two-year mark. Those whose preferred language was English were more likely to disengage ( n = 47, 31.5%) than those whose preferred language was French ( n = 35, 18.5%; χ2 = 9.11, p < .01). This remained significant in the multivariate regression. In focus groups, participants identified language as one aspect of a complex communication process between patients and clinicians and highlighted the importance of culture in the clinical encounter. Language status of patients plays an important role in their engagement with early psychosis services. Our findings underscore the value of establishing communication and cultural understanding in creating clinical/therapeutic alliance.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 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".