Implementation of strengths model case management in seven mental health agencies in Canada: Direct‐service practitioners' implementation experience
Bibliographic record
Abstract
RATIONALE: Implementation of strengths model case management is increasing internationally. However, few studies have focused on its implementation process, and none have specifically addressed the implementation experience of direct-service practitioners. OBJECTIVE: This paper presents factors that facilitate and impede the successful implementation of the strengths model, with a specific focus on practitioners who deliver the intervention directly to service recipients. METHOD: To address this objective, a qualitative study of seven mental health agencies that implemented the model was conducted, involving a combination of participant observations and qualitative semistructured interviews with case managers, team supervisors, and senior managers. Qualitative data were analyzed using open coding followed by axial coding. Finally, the findings were aligned with an adapted Consolidated Framework for Implementation Research. RESULTS: Implementation of the strengths model involved a significant change in practice for case management practitioners. The results confirm that at the beginning of implementation, the strengths model was perceived as complex and not always adaptable to on-the-ground realities. With time, and with support from management, ongoing training and supervision sessions, and reflection and discussion, practitioners regained feelings of competence and resistance to the model diminished. The use of the model's structured team-based supervision tools was fundamental to supporting the implementation process by enabling an interactive and concrete training approach. CONCLUSIONS: The more an approach leads to changes in daily practice and is perceived as complex, the more concrete support is needed during implementation. This article highlights the importance of attending to a practitioner's sense of personal effectiveness and competence in the adoption of new practices.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".