Developmental evaluation as a strategy to enhance the uptake and use of deprescribing guidelines: protocol for a multiple case study
Bibliographic record
Abstract
BACKGROUND: The use of developmental evaluation is increasing as a method for conducting implementation research. This paper describes the use of developmental evaluation to enhance an ongoing study. The study develops and implements evidence-based clinical guidelines for deprescribing medications in primary care and long-term care settings. A unique feature of our approach is our use of a rapid analytical technique. METHODS/DESIGN: The team will carry out two separate analytical processes: first, a rapid analytical process to provide timely feedback to the guideline development and implementation teams, followed by a meta-evaluation and second, a comprehensive qualitative analysis of data after the implementation of each guideline and a final cross-case analysis. Data will be gathered through interviews, through observational techniques leading to the creation of field notes and narrative reports, and through assembling team documents such as meeting minutes. Transcripts and documents will be anonymized and organized in NVIVO by case, by sector (primary care or long-term care), and by implementation site. A narrative case report, directed coding, and open coding steps will be followed. Clustering and theming will generate a model or action map reflecting the functioning of the participating social environments. DISCUSSION: In this study, we will develop three deprescribing guidelines and will implement them in six sites (three family health teams and three long-term care homes), in a sequential iterative manner encompassing 18 implementation efforts. The processes of 11 distinct teams within four conceptual categories will be examined: a guideline priority-setting group, a guideline development methods committee, 3 guideline development teams, and 6 guideline implementation teams. Our methods will reveal the processes used to develop and implement the guidelines, the role and contribution of developmental evaluation in strengthening these processes, and the experience of six sites in implementing new evidence-based clinical guidelines. This research will generate new knowledge about team processes and the uptake and use of deprescribing guidelines in family health teams and long-term care homes, with a goal of addressing polypharmacy in Canada. Clinicians and researchers creating clinical guidelines to introduce improvements into daily practice may benefit from our developmental evaluation approach.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.163 | 0.134 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".