The Development of a Community Pharmacy-Based Intervention to Optimize Patients’ Use of and Experience with Antidepressants: A Step-by-Step Demonstration of the Intervention Mapping Process
Bibliographic record
Abstract
Objective: To describe the development of a community pharmacy-based intervention aimed at optimizing experience and use of antidepressants (ADs) for patients with mood and anxiety disorders. Methods: Intervention Mapping (IM) was used for conducting needs assessment, formulating intervention objectives, selecting change methods and practical applications, designing the intervention, and planning intervention implementation. IM is based on a qualitative participatory approach and each step of the intervention development process was conducted through consultations with a pharmacists’ committee. Results: A needs assessment was informed by qualitative and quantitative studies conducted with leaders, pharmacists, and patients. Intervention objectives and change methods were selected to target factors influencing patients’ experience with and use of ADs. The intervention includes four brief consultations between the pharmacist and the patient: (1) provision of information (first AD claim); (2) management of side effects (15 days after first claim); (3) monitoring treatment efficacy (30-day renewal); (4) assessment of treatment persistence (2-month renewal, repeated every 6 months). A detailed implementation plan was also developed. Conclusion: IM provided a systematic and rigorous approach to the development of an intervention directly tied to empirical data on patients’ and pharmacists’ experiences and recommendations. The thorough description of this intervention may facilitate the development of new pharmacy-based interventions or the adaptation of this intervention to other illnesses and settings.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".