Developing a spectrum model of engagement in services for first episode psychosis: beyond attendance
Bibliographic record
Abstract
Background: Early intervention services (EIS) for psychosis have proven highly effective in treating first episode psychosis. Yet, retention or "engagement" in EIS remains highly variable. Dis/engagement as a contested concept and fluid process involving relationships between service providers and service users remains poorly understood. This study aimed to critically evaluate and explain the dynamic interplay of service provider-user relationships in effecting dis/engagement from an early intervention program for psychosis. Methods: Forty study participants, 16 service providers and 24 service users (19 current and 5 disengaged) from a Canadian EIS program, were administered semi-structured interviews. Qualitative analysis was conducted using grounded theory methods, with findings captured and reconceptualized in a novel explanatory model. Findings: A model of engagement with eight major domains of engagement in EIS positioned along a control-autonomy spectrum was developed from the findings, with Clinical engagement (attendance) and Life engagement (life activities) at opposite ends of the spectrum, interspersed by six intermediate domains: Medication/treatment, Symptoms/illness, Mental health, Physical health/wellness, Communication, and Relationships, each domain bearing uniquely on engagement. Conclusions: An examination of service user and service provider perspectives on the various domains identified in the spectrum model, and their dynamic interplay, reveals the complexity of choices faced by service users in engaging and not engaging with services.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".