Modeling variables associated with personal recovery among service users with mental disorders using community-based services
Bibliographic record
Abstract
BACKGROUND: Mental health research is evolving toward the identification of conceptual models and associated variables, which may provide a better understanding of personal recovery, given its importance for individuals affected by mental disorders (MDs). AIMS: This article evaluated personal recovery in a sample of adults with MDs using an adapted conceptual framework based on the Andersen behavioral model, which evaluates predisposing, enabling and needs factors in service use. METHODS: The study design was cross-sectional and included 327 mental health service users recruited across four local health service networks in Quebec (Canada). Data were collected using seven standardized instruments and participant medical records. Structural equation modeling was performed. RESULTS: Quality of life (QOL), an enabling factor, was most strongly associated with personal recovery. Health behavior variables associated with recovery included the following: use of alcohol services, having a family physician, consulting a psychologist, use of food banks, consulting fewer professionals and not using drug services. Regarding needs factors, higher numbers of needs, lower severity of unmet health, social and basic needs and absence of mood disorders were also associated with personal recovery. No predisposing factors emerged as significant in the model. CONCLUSION: Findings suggest that QOL, needs variables and comprehensive service delivery are important in personal recovery. Services should be individualized to the health, social and basic needs of service users, particularly those with mood disorders or co-occurring mental health/substance use disorders.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".