Patient satisfaction with mental health services based on Andersen’s Behavioral Model
Bibliographic record
Abstract
OBJECTIVE: The purpose of this article was to assess the satisfaction of adult patients who received mental health services (MHS) in healthcare networks staffed by multidisciplinary professionals and offering a range of MHS, and to identify variables associated with patient satisfaction. METHODS: This cross-sectional study included 325 patients with mental disorders (MDs) among 4 Quebec health service networks. Data were collected using 9 standardized instruments and participant medical records. A 3-factor conceptual framework (predisposing, enabling, and needs-related factors) based on Andersen's Behavioral Model was used, integrating sociodemographic, clinical, needs-related, service utilization, social support, and quality-of-life (QOL) variables. An adjusted multiple linear regression model was performed. RESULTS: The global mean score for patient satisfaction was 4.11 (minimum: 2.0; maximum: 5.0). Among the enabling factors, continuity of care, having a case manager, and help received from services were positively associated with patient satisfaction, whereas being hospitalized was negatively associated. Among the needs-related factors, the number of needs was negatively associated with satisfaction. CONCLUSIONS: Findings demonstrated higher levels of satisfaction among patients who received good continuity of care and well-managed, frequent services in relation to their needs. Dissatisfaction was higher for patients with serious unmet needs or those hospitalized, which underlines the importance of taking these particular variables into account in the interest of improving MHS delivery and patient recovery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".