Survey content validation evaluating the dissemination and implementation of deprescribing guidelines
Bibliographic record
Abstract
BACKGROUND: Policies, protocols and processes within organisations can facilitate or hinder guideline adoption. There is limited knowledge on the strategies used by organisations to disseminate and implement evidence-based deprescribing guidelines or their impact. METHODS: We aimed to develop an online survey targeting key organisations involved in deprescribing guideline endorsement, dissemination, modification or translation internationally. Survey questions were drafted, mirroring the six components of the reach, effectiveness, adoption, implementation and maintenance (RE-AIM) framework. Content validation was undertaken and established by a panel of clinicians, researchers and implementation experts. RESULTS: A 52-item survey underwent two rounds of content validation. The minimum threshold (I-CVI > 0.78) for relevance and importance was met for 39 items (75%) in the first round and 44 of 48 items (92%) in the second round. The expert panel concluded that the adoption, implementation and effectiveness survey sections were largely relevant and important to this topic, whereas the reach and maintenance sections were harder to understand and may be less pertinent to the research question. CONCLUSIONS: A 44-item survey investigating dissemination and implementation strategies for deprescribing guidelines has been developed and its content validated. Widespread survey distribution may identify effective strategies and inform dissemination and implementation planning for newly developed guidelines.
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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 | 0.328 | 0.340 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".