What Do We Know about Knowledge Brokers in Paediatric Rehabilitation? A Systematic Search and Narrative Summary
Bibliographic record
Abstract
PURPOSE: To conduct a systematic review of the literature related to the use of knowledge brokers within paediatric rehabilitation, and specifically to determine (1) how knowledge brokers are defined and used in paediatric rehabilitation and (2) whether knowledge brokers in paediatric rehabilitation have demonstrably improved the performance of health care providers or organizations. METHODS: The MEDLINE, CINAHL, EMBASE, and AMED databases were systematically searched to identify studies relating to knowledge brokers or knowledge brokering within paediatric rehabilitation, with no restriction on the study design or primary aim. Following review of titles and abstracts, those studies identified as potentially relevant were assessed based on the inclusion criteria that they: (1) examined some aspect of knowledge brokers/brokering in paediatric rehabilitation; (2) included sufficient descriptive detail on how knowledge brokers/brokering were used; and(3) were peer-reviewed and published in English. RESULTS: Of 1513 articles retrieved, 4 met the inclusion criteria, 3 of which referenced the same knowledge broker initiative. Two papers used mixed methods, one qualitative methodology, and one case presentation. Because of the different methods used in the included studies, the findings are presented in a narrative summary. CONCLUSIONS: This study provides an overview of the limited understanding of knowledge brokers within paediatric rehabilitation. Knowledge broker initiatives introduced within paediatric rehabilitation have been anchored in different theoretical frameworks, and no conclusions can be drawn as to the optimum combination of knowledge brokering activities and methods, nor about optimal duration, for sustained results.
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 | 0.040 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 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".