A protocol for a systematic review of knowledge translation strategies in the allied health professions
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) aims to close the gap between knowledge and practice in order to realize the benefits of research through (a) improved health outcomes, (b) more effective health services and products, and (c) strengthened healthcare systems. While there is some understanding of strategies to put research findings into practice within nursing and medicine, we have limited knowledge of KT strategies in allied health professions. Given the interprofessional nature of healthcare, a lack of guidance for supporting KT strategies in the allied health professions is concerning. Our objective in this study is to systematically review published research on KT strategies in five allied health disciplines. METHODS: A medical research librarian will develop and implement search strategies designed to identify evidence that is relevant to each question of the review. Two reviewers will perform study selection and quality assessment using standard forms. For study selection, data will be extracted by two reviewers. For quality assessment, data will be extracted by one reviewer and verified by a second. Disagreements will be resolved through discussion or third party adjudication. Within each profession, data will be grouped and analyzed by research design and KT strategies using the Effective Practice and Organisation of Care Review Group classification scheme. An overall synthesis across professions will be conducted. SIGNIFICANCE: A uniprofessional approach to KT does not represent the interprofessional context it targets. Our findings will provide the first systematic overview of KT strategies used in allied health professionals' clinical practice, as well as a foundation to inform future KT interventions in allied healthcare settings.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.185 | 0.231 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.111 | 0.026 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".