Perceived Barriers and Facilitators to Using Knowledge Brokers in Canadian Rehabilitation Settings: A Qualitative Study
Bibliographic record
Abstract
Abstract Background: Knowledge translation experts advocate for employing knowledge brokers (KBs) to promote the uptake of research evidence in health care settings. However, little is known about factors influencing the utilization of KBs, thereby limiting their employment within healthcare organizations. This research aimed to identify factors likely to hinder or promote the optimal use of KBs within rehabilitation settings in Canada.Methods: Qualitative study using semi-structured telephone interviews with individuals performing KB activities in rehabilitation settings across Canada. The interview topic guide was informed by the Consolidated Framework for Implementation Research (CFIR) and consisted of 20 questions covering five domains (characteristics of individuals, inner setting, process, outer settings, and innovation characteristics). All interviews lasted 60 to 90 minutes, were digitally recorded, and transcribed verbatim. We conducted qualitative descriptive analysis combining deductive coding guided by the CFIR. Two independent analysts coded and rated all interviews, then met to review, deliberate and modify the codes as appropriate. A matrix was created by listing the salient codes for each CFIR construct to identify factors (facilitators and barriers) at the individual, organisational, and process level most likely to impact the KB’s success/failure.Results: Twenty-three participants, from five Canadian provinces were interviewed. At the individual level, the majority of participants reported having strong communication skills, being confident about performing KBs activities, and possessing solid clinical experience and prior research skills. At the organizational level, most respondents indicated constantly networking and engaging with clinical teams and different stakeholders, and having an acceptable level of guidance from their managers. Very few participants felt that they received sufficient organizational support (i.e., clerical support and IT support). At the process level, all participants indicated needing evaluation tools to better gauge their performance, and the majority mentioned that they would benefit from having additional training tailored to their roles as KBs.Conclusions: Individual, organisational and process level factors likely to hinder or promote the optimal use of KBs within Canadian rehabilitation settings include skillsets and networking abilities; culture, resources, and leadership support; and the need for specific training for KBs and for evaluation tools to monitor their performance.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".