General Practice Patients’ Experiences and Perceptions of the WiserAD Structured Web-Based Support Tool for Antidepressant Deprescribing: Protocol for a Mixed Methods Case Study With Realist Evaluation
Bibliographic record
Abstract
BACKGROUND: Research suggests that the rapid increase in worldwide antidepressant use is mainly due to a rise in long-term and potentially inappropriate use. It has been suggested that 1 in 3 antidepressant users among general practice patients are no longer experiencing clinical benefits from their medication and should commence deprescribing. However there are many barriers to antidepressant deprescribing for both patients and clinicians, which adds to the complex nature of reducing or ceasing the medication. As such, antidepressant deprescribing does not routinely occur in clinical practice. Evidence-based supports and interventions for safe and successful antidepressant deprescribing are needed to assist patients and their doctors. Interventions should also include an understanding of how an intervention works, why it works, and whom it is for. OBJECTIVE: This study aims to evaluate how the WiserAD approach to antidepressant deprescribing works, whom it is for, and the underlying circumstances by (1) examining the experiences and perceptions of WiserAD among antidepressant users, (2) identifying the underlying mechanisms of the WiserAD approach to antidepressant deprescribing, and (3) describing in what contexts and to what extent the underlying mechanisms of WiserAD are suited for antidepressant users. METHODS: A mixed methods case study with realist evaluation will be conducted among participants in the WiserAD randomized controlled trial for antidepressant deprescribing. Quantitative data will be obtained from up to 12 participants from the intervention and control arms at baseline and 3-month follow-up. Baseline data will be used to characterize the sample using descriptive statistics. Paired samples t tests will also be performed to compare responses between baseline and 3-month follow-up for participant self-management, skills, confidence and knowledge, beliefs about medicines, current emotional health, and well-being symptoms. Qualitative data from the same participants will be collected via narrative interview at 3-month follow-up. Quantitative and qualitative data will be converged to form a "case," and analysis will be conducted within each case with comparisons made across multiple cases. RESULTS: Recruitment of participants commenced in October 2022 and will be completed by March 2023. Analysis will be completed by June 2023. CONCLUSIONS: To our knowledge, this will be the first realist evaluation of an antidepressant deprescribing intervention in general practice. Findings from this evaluation may assist in the implementation of the WiserAD approach to antidepressant deprescribing in routine clinical practice. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/42526.
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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 | 0.067 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".