Relationships Among Structures, Team Processes, and Outcomes for Service Users in Quebec Mental Health Service Networks
Bibliographic record
Abstract
BACKGROUND: Few studies have identified and compared profiles of mental health service networks (MHSN) in terms of structures, processes, and outcomes, based on cluster analyses and perceptions of team managers, MH professionals and service users. This study assessed these associations in Quebec metropolitan, urban and semi-urban MHSN. METHODS: A framework adapted from the Donabedian model guided data management, and cluster analyses were used to identify categories. Study participants included team managers (n = 45), MH professionals (n = 311) and service users (n = 327). RESULTS: For all three MHSN, a common outcome category emerged: service users with complex MH problems and negative outcomes. The Metropolitan network reported two categories for structures (specialized MH teams, primary care MH teams) and processes (senior medical professional, psychosocial professionals), and outcomes (middle-age men with positive outcomes, older women with few MH problems). The Urban and Semi-urban networks revealed one category for structures (all teams) and service user (young service users with drug disorders), but two for processes (psychosocial professionals: urban, all professionals: semi-urban). CONCLUSION: The Metropolitan MHSN showed greater heterogeneity regarding structures and team processes than the other two MHSN. Service user outcomes were largely associated with clinical characteristics, regardless of network configurations for structures and team processes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".