‘Changing Minds’: determining the effectiveness and key ingredients of an educational intervention to enhance healthcare professionals’ intentions to prescribe physical activity to patients with physical disabilities
Bibliographic record
Abstract
BACKGROUND: Healthcare professionals (HCPs) are vital conduits of leisure-time physical activity (LTPA) information; however, few discuss LTPA with their patients with disabilities. 'Changing Minds, Changing Lives' (CMCL) is a nationwide, theory- and evidence-based seminar aimed at increasing LTPA-discussion among HCPs by enhancing their attitudes, subjective norms, perceived behavioural control (PBC), and intentions. The purposes of the current study were to: examine the effectiveness and short- and long-term maintenance of a CMCL seminar on HCPs' social cognitions to discuss LTPA; and explore key implementation variables that predict changes in HCPs' social cognitions. METHODS: Prior-to, as well as immediately, one, and six months following a CMCL seminar, 97 HCPs (Mage ± SD = 36.23 ± 10.42; 69.0% female; 97.9% Caucasian; 38.1% rehabilitation therapists; years in profession = 11.56 ± 9.94) from five Canadian provinces completed questionnaires that assessed the Theory of Planned Behaviour constructs with regard to discussing LTPA with their patients with a physical disability. Key presenter characteristics and intervention delivery components were extracted from presenter demographic questionnaires and seminar checklists, respectively. Separate repeated-measures ANOVAs and post-hoc t-tests evaluated changes in HCPs' social cognitions. Hierarchical multiple regressions were conducted to predict intentions and to understand which implementation variables may help explain significant changes in social cognitions. RESULTS: Significant increases in HCPs' social cognitions for discussing LTPA were reported from pre- to post-seminar (ps <0.002); however, increases were not maintained at follow-up. PBC emerged as the strongest predictor of participants' post-CMCL intentions (β = 0.45, p <0.001). Although several implementation characteristics were related to changes in perceptions, the number of seminars the presenter delivered was the only significant negative predictor of post-seminar PBC (β = -0.18, p <0.05). CONCLUSIONS: Future iterations of the CMCL intervention should include additional strategies to sustain improvements in HCPs' social cognitions over time. Future CMCL evaluations should measure additional implementation variables so that the key ingredients for 'Changing Minds' can continue to be investigated.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".