Examining the Effectiveness of Coaching Behaviour Change Techniques among University Students with Disabilities: A Preliminary Study
Bibliographic record
Abstract
Introduction: Behaviour change techniques (BCTs) are designed to facilitate behaviour change, but their effectiveness in interventions is inconsistent. We in fact have little knowledge on whether participants change their BCT usages after being coaching in an intervention. This study aimed to preliminary examine changes in BCTs among students with disabilities participating in BCT coaching sessions in an adapted physical activity program (FAM). Methods: A 10-week program supported N= 9 university students (>18) with disabilities in increasing their physical activity through coaching BCTs including action planning and goal setting. Participants completed an action planning questionnaire and (n=6) wrote their physical activity goals pre and post program. Each goal was then coded as either behavioural or outcome-oriented goals and whether they were specific, measurable, and time-bound. Results: Action planning improved from pre coaching (M= 15.78, SD =4.68), to post coaching (M= 16.33, SD =9.90). Medium changes (dRMpooled=0.32) were reported from pre to mid-program, but only small effects (dRMpooled=0.006) were found from pre to end- of program. Before coaching, 83% participants' goals were outcome-oriented, and 17% were behaviour-oriented. Post coaching, behaviour goals increased to 67% and outcome goals decreased to 33%. Specific and time-bound goals accounted for 17% each, while measurable goals were 67%. Goal specificity improved to 50%, time-bound goals increased to 67% while goal measurability remained consistent 67%. Conclusion: Our first try in coaching BCTs found preliminary effects on changing goal setting, while improvements in action planning only at mid-program. This can provide guidance for future behaviour change coaching interventions.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".