Geriatrics, interprofessional practice, and interorganizational collaboration: a knowledge-to-practice intervention for primary care teams.
Bibliographic record
Abstract
INTRODUCTION: Caring for frail seniors requires health professionals with skills and knowledge in 3 core competencies: geriatrics, interprofessional practice, and interorganizational collaboration. Despite a growing population of frail seniors in all developed countries, significant gaps exist in preparation of health professionals in these skills. To help close these gaps, a knowledge-to-practice (KTP) process was undertaken to increase the capacity of newly created family health teams and longer standing Community Health Centers in the Province of Ontario, Canada. METHODS: Each team identified a staff member to become its facilitator in the 3 core skill sets. Guided by a KTP framework, a set of training modules were created, compiled into a digital toolkit for transfer into practice, translated in a multimethods workshop, and implemented using a variety of strategies to optimize practice change. RESULTS: Staff from 82% of the targeted primary care teams learned to use the toolkit in a train-the-facilitator process that was highly valued, and prompted a range of changes in personal and team practice. A digital toolkit for primary care teams remains an enduring and often used resource. DISCUSSION: Closing the knowledge gap in the core competencies for frailty focused care is complex. A KTP framework helped guide a staged multimethod process that produced both individual and team practice change and on online toolkit that has a continuing influence.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".