“From Health Experts to Health Guides”: Motivational Interviewing Learning Processes and Influencing Factors
Bibliographic record
Abstract
Motivational interviewing is an evidence-based counseling approach. However, its learning processes and their influencing factors are understudied, failing to address the suboptimal use of motivational interviewing in clinical practice. A participatory action research was conducted in collaboration with 16 primary care clinicians, who encountered similar challenges through their previous counseling approaches. The study aimed to facilitate and describe the clinicians' professional transformation through interprofessional communities of practice on motivational interviewing (ICP-MI). Data were collected using the principal investigator's research journal and participant observation of four independent ICP-MIs (76 h) followed by focus groups (8 h). The co-participants performed inductive qualitative data analysis. Results report that learning motivational interviewing requires a paradigm shift from health experts to health guides. The learning processes were initiated by the creation of an openness to the MI spirit and rapidly evolved into iterative processes of MI spirit embodiment and MI skill building. The intrinsic influencing factors involved the clinician's personal traits and professional background; the extrinsic influencing factor was the shared culture disseminating the expert care model. Previously described in a fragmented manner, motivational interviewing learning processes, and its influencing factors were presented as integrated findings. Considerations in elaborating effective MI training/implementation programs are discussed for clinicians, trainers, and decision-makers. Future areas of investigation are also highlighted calling forth the research community to contribute to knowledge advancement on health education in primary care.
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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 | 0.033 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".