Knowledge translation and interprofessional collaboration: Where the rubber of evidence-based care hits the road of teamwork
Bibliographic record
Abstract
Knowledge-translation interventions and interprofessional education and collaboration interventions all aim at improving health care processes and outcomes. Knowledge-translation interventions attempt to increase evidence-based practice by a single professional group and thus may fail to take into account barriers from difficulties in interprofessional relations. Interprofessional education and collaboration interventions aim to improve interprofessional relations, which may in turn facilitate the work of knowledge translation and thus evidence-based practice. We summarize systematic review work on the effects of interventions for interprofessional education and collaboration. The current evidence base contains mainly descriptive studies of these interventions. Knowledge is limited regarding the impact on care and outcomes and the extent to which the interventions increase the practice of evidence-based care. Rigorous multimethod research studies are needed to develop and strengthen the current evidence base in this field. We describe a Health Canada-funded randomized trial in which quantitative and qualitative data will be gathered in 20 general internal medicine units located at 5 Toronto, Ontario, teaching hospitals. The project examines the impact of interprofessional education and collaboration interventions on interprofessional relationships, health care processes (including evidence-based practice), and patient outcomes. Routes are suggested by which interprofessional education and collaboration interventions might affect knowledge translation and evidence-based practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".