From a “Terrifying Challenge” to a “Professional Revelation” - Implementing Motivational Interviewing through Participatory Action Research
Bibliographic record
Abstract
Abstract Background Motivational Interviewing (MI) is a humanistic and evidence-based counseling approach within primary care. However, MI rarely translates to clinical practice that follows the usual introductory training programs; a lack of evidence regarding its implementation persists today. A participatory action research was conducted to (1) facilitate and describe the clinicians’ professional transformation through interprofessional communities of practice on motivational interviewing (ICP-MI), and (2) explore the contribution of ICP-MI in transforming their daily practices. This article addresses the first objective. Methods Data collection involved the principal investigator’s research journal, participant observation of four ICP-MIs (76 hours, 16 clinicians), and four appraisal focus groups. A general inductive approach was used for qualitative data analysis. Results Findings describe the four processes of MI implementation in primary care as motivational endeavors: ambivalence, introspection, experimentation, and mobilization. The clinicians were initially ambivalent with respect to MI implementation, taking into consideration the significant challenges involved. After introspecting previous practices, they realized the limits of their clinician-centered counseling approach, which consolidated their engagement in ICP-MI. Thus, the experimentation of MI implementation initiatives in the workplace followed and enabled clinicians to witness the feasibility and effectiveness of MI. Finally, the clinicians were intrinsically mobilized to ensure MI sustainability in their practices. Two categories of influencing factors were reported. Intrinsic factors included personal traits, and perception about MI as a clinical priority. Extrinsic factors related to organizational support that was crucial in providing the appropriate resources and supporting the clinicians’ implementation efforts. Results are discussed according to the Consolidated Framework for Implementation Research (CFIR). Conclusions As described in a fragmented manner in previous studies, MI implementation processes and influencing factors are presented in our study as integrated findings; we also suggest innovative avenues for future research projects. Considerations in elaborating effective training programs are highlighted, especially when it comes to providing motivational and organizational support to succeed at MI implementation within primary care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".