Use of a knowledge exchange event strategy to identify key priorities for implementing deprescribing in primary healthcare in Nova Scotia, Canada
Bibliographic record
Abstract
Background: Deprescribing, the process of dose reduction or stopping of medication(s) that may no longer be required, may improve medication use and patient outcomes. A collaborative interprofessional deprescribing research team was formed in 2017 in Nova Scotia (NS), Canada with the goal of investigating potential deprescribing initiatives which could be translated to primary healthcare in NS. The knowledge-to-action framework, which includes knowledge exchange, was used to guide the work of this team. Preliminary work involved knowledge inquiry and synthesis through a scoping review of deprescribing strategies in primary healthcare, a qualitative study to understand influences on deprescribing by local practitioners, and an analysis that combined the two. Aims and objectives: To describe and reflect on how an interactive knowledge exchange event strategy was used to (1) share the results, including knowledge tools, of previously conducted deprescribing research with stakeholders; (2) identify priorities for the development and implementation of collaborative deprescribing strategies in primary healthcare in NS. Key conclusions: The knowledge exchange event strategy utilised in this project achieved the planned objectives of sharing research results, raising awareness about deprescribing, and providing direction for future initiatives. The successful implementation of the knowledge exchange event hinged on many factors such as hiring a research coordinator; limiting the in-person event to one half-day; and using a variety of strategies for participant engagement both before and after the event. Other research teams could adopt a similar knowledge exchange event process as an approach for sharing research results and identifying future research and translation priorities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".