What is known about the role of external facilitators during the implementation of complex interventions in healthcare settings? A scoping review
Bibliographic record
Abstract
OBJECTIVE: To synthesise current knowledge about the role of external facilitators as an individual role during the implementation of complex interventions in healthcare settings. DESIGN: A scoping review was conducted. We reviewed original studies (between 2000 and 2023) about implementing an evidence-based complex intervention in a healthcare setting using external facilitators to support the implementation process. An information specialist used the following databases for the search strategy: MEDLINE, CINAHL, APA PsycINFO, Academic Search Complete, EMBASE (Scopus), Business Source Complete and SocINDEX. RESULTS: 36 reports were included for analysis, including 34 different complex interventions. We performed a mixed thematic analysis to synthesise the data. We identified two primary external facilitator roles: lead facilitator and process expert facilitator. Process expert external facilitators have specific responsibilities according to their role and expertise in supporting three main processes: clinical, change management and knowledge/research management. CONCLUSIONS: Future research should study processes supported by external facilitators and their relationship with facilitation strategies and implementation outcomes. Future systematic or realist reviews may also focus on outcomes and the effectiveness of external facilitation.
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 | 0.065 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".