The Influence of Contextual Factors on the Process of Formulating Strategies to Improve the Adoption of Care Manager Activities by Primary Care Nurses
Bibliographic record
Abstract
BACKGROUND: Primary care nurses are well-suited to provide care management for common mental disorders, but their practices depend on context. Various strategies can be considered to improve the adoption of nursing care manager activities, but data from implementation studies rarely address strategy formulation. AIM: To analyze the influence of contextual factors on strategy formulation to improve the adoption of care manager activities by primary care nurses. METHOD: A qualitative multiple case study in three primary care clinics was carried out. Data were collected through individual interviews (n = 32) and observations (n = 7), working group meetings, and relevant documents. Thematic analysis was conducted. RESULTS: Contextual factors influenced strategy formulation through organizational readiness for change, which resulted from tension for change and perceived organizational ability to implement change. Tension for change was generated through the perceived gap between patient needs and service availability, perceived compatibility with the nurses work environment, and their assessment of their capacity to perform care manager activities or acquire the necessary skills. CONCLUSION: Future studies should give sufficient attention to implementation strategy formulation and consider the dynamic role of organizational readiness for change when facilitating the adoption of evidence-based practices for common mental disorders in primary care.
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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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".