Motivational interviewing to facilitate goal setting in rehabilitation: a feasibility study
Bibliographic record
Abstract
Purpose To investigate the feasibility of using embedded motivational interviewing (MI) to develop patient-centred goals in rehabilitation.Method Sixty adults (mean age 68 years, 60% female) referred with any health condition for community rehabilitation and four MI trained clinicians participated to inform feasibility of embedding motivational interviewing in goal setting to facilitate patient-centred discussions. Feasibility domains of acceptability, demand, implementation (including MI fidelity), practicality and limited efficacy were evaluated.Results Over the 14-month recruitment period, 70 patients were eligible and 60 agreed to participate (86% uptake). Patient participants reported high levels of acceptance (median 10/10, IQR 9 to 10) and identified a median of 2 (IQR 2 to 4) patient-centred goals, of which 69% were achieved at discharge. MI goal setting took a median of 20 mins (IQR 17 to 24) and most commonly occurred during the second rehabilitation session (n = 28, 47%). There were no adverse events and no instances where goal setting was incomplete. Clinicians proficiently integrated MI into clinical practice and supported the application of MI within routine rehabilitation goal setting.Conclusion Integrating motivational interviewing into rehabilitation goal setting was a feasible way to elicit patient-centred goals, which were accepted by patients and rehabilitation clinicians.
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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 | 0.035 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".