Translating Evidence into Practice: The Case of Dementia Guidelines in Specialized Geriatric Services
Bibliographic record
Abstract
This paper describes a process of identifying best practice guidelines for non-pharmacological management for individuals with dementia and disseminating them to a group of frontline practitioners in specialized geriatric services. Our dissemination plan involved early participation of practitioners, development of a chart summarizing five guidelines showing commonalities, contrasts, and gaps in the guidelines, and their interpretation, colour-coded for the strength of the evidence on which they were based. Two sequential workshops were held in which recommendations for action were developed. Outcomes of the process included a resource manual, a network of practitioners, and action recommendations based upon survey data. An early follow-up evaluation showed increased adoption of 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 | 0.503 | 0.694 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.019 | 0.045 |
| Scholarly communication | 0.040 | 0.049 |
| Open science | 0.015 | 0.040 |
| Research integrity | 0.048 | 0.039 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".