Motivational interviewing: a novel intervention for translating rehabilitation research into practice
Bibliographic record
Abstract
PURPOSE: Despite recent advances in rehabilitation research, moving evidence into clinical practice remains a challenge. This article explores a novel approach to knowledge translation (KT) - motivational interviewing (MI). MI is a style of communication that is typically used to facilitate health related behavior change in patients. Here we explore its potential use as a KT intervention aimed at clinicians. METHODS: Commentary. Relevant literature on MI and KT is summarized and discussed by considering how MI could be used in a KT strategy aimed at rehabilitation clinicians. RESULTS: Clinician motivation and readiness to change are key issues influencing implementation of evidence-based practice. We provide an argument suggesting that clinicians' readiness to change clinical practices can potentially be enhanced through MI. The MI conceptual framework, principles, and strategies, typically used in patients, are discussed here in a novel context - enhancing clinician change in practice. CONCLUSIONS: MI is an effective intervention when the goal is to motivate individuals to change a current behavior. We suggest that MI is an evidence-based intervention that has been proven to be effective with patients and warrants study as a promising KT intervention. IMPLICATIONS FOR REHABILITATION: • Despite recent advances in rehabilitation research, moving evidence into practice remains a challenge. • Clinician motivation is one key issue influencing the implementation of evidence-based practice. • Clinician motivation to implement evidence-based practice can potentially be enhanced through an approach called motivational interviewing (MI). • Motivational interviewing is an evidence-based intervention that has proven to be effective in promoting behavioral change in patients, and warrants study in terms of its potential as a KT intervention.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".