Introducing an interactional approach to exploring facilitation as an implementation intervention: examining the utility of Conversation Analysis
Bibliographic record
Abstract
BACKGROUND: The widely adopted integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) framework identifies facilitation as a 'core ingredient' for successful implementation. Indeed, most implementation scientists agree that a certain degree of facilitation is required to translate research into clinical practice; that is, there must be some intentional effort to assist the implementation of evidence-based approaches and practices into healthcare. Yet understandings of what constitutes facilitation and how to facilitate effectively remain largely theoretical and, therefore, provide scant practical guidance to ensure facilitator success. Implementation Science theories and frameworks often describe facilitation as an activity accomplished in, and through, formal and informal communication amongst facilitators and those involved in the implementation process (i.e. 'recipients'). However, the specific communication practices that constitute and enable effective facilitation are currently inadequately understood. AIM: In this debate article, we argue that without effective facilitation-a practice requiring significant interactional and interpersonal skills-many implementation projects encounter difficulties. Therefore, we explore whether and how the application of Conversation Analysis, a rigorous research methodology for researching patterns of interaction, could expand existing understandings of facilitation within the Implementation Science field. First, we illustrate how Conversation Analysis methods can be applied to identifying what facilitation looks like in interaction. Second, we draw from existing conversation analytic research into facilitation outside of Implementation Science to expand current understandings of how facilitation might be achieved within implementation. CONCLUSION: In this paper, we argue that conversation analytic methods show potential to understand and refine facilitation as a critical, and inherently interactional, component of implementation efforts. Conversation analytic investigations of facilitation as it occurs in real-time between participants could inform mechanisms to (1) improve understandings of how to achieve successful implementation through facilitation, (2) overcome difficulties and challenges in implementation related to interpersonal communication and interaction, (3) inform future facilitator training and (4) inform refinement of existing facilitation theories and frameworks (e.g. i-PARIHS) currently used in implementation interventions.
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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 | 0.105 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| 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".