Facilitation roles and characteristics associated with research use by healthcare professionals: a scoping review
Bibliographic record
Abstract
BACKGROUND: Implementing research findings into practice is a complex process that is not well understood. Facilitation has been described as a key component of getting research findings into practice. The literature on facilitation as a practice innovation is growing. This review aimed to identify facilitator roles and to describe characteristics of facilitation that may be associated with successful research use by healthcare professionals. METHODS: We searched 10 electronic databases up to December 2016 and used predefined criteria to select articles. We included conceptual papers and empirical studies that described facilitator roles, facilitation processes or interventions, and that focused on healthcare professionals and research use. We used content and thematic analysis to summarise data. Rogers' five main attributes of an innovation guided our synthesis of facilitation characteristics. RESULTS: Of the 38 488 articles identified from our online and manual search, we included 195 predominantly research studies. We identified nine facilitator roles: opinion leaders, coaches, champions, research facilitators, clinical/practice facilitators, outreach facilitators, linking agents, knowledge brokers and external-internal facilitators. Fifteen facilitation characteristics were associated with research use, which we grouped into five categories using Rogers' innovation attributes: relative advantage, compatibility, complexity, trialability and observability. CONCLUSIONS: We found a diverse and broad literature on the concept of facilitation that can expand our current thinking about facilitation as an innovation and its potential to support an integrated, collaborative approach to improving healthcare delivery.
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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 | 0.056 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.034 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".