Using the Capability, Opportunity, and Motivation (COM-B) Model to Understand Physical Activity Prescribing Behaviour among Healthcare Providers
Bibliographic record
Abstract
One promising method of addressing declining physical activity (PA) participation is prescription-based-PA whereby healthcare providers (HCPs) write movement recommendations tailored to patient needs. Prescription-to-Get-Active (RxTGA), a not-for-profit organization based in Alberta, Canada, allows HCPs to prescribe PA to patients who do not meet national activity guidelines. The Capability, Opportunity, and Motivation (COM-B) Behaviour Model is a health-promotion framework that involves examining one’s perceived capability, opportunity, and motivation to engage in a behaviour, such as PA. To date, no studies have examined PA prescribing behaviour using the COM-B model in an established community-based program. Thus, the purpose of this correlational study was to examine the relationships between perceived Capability, Opportunity, and Motivation to prescribe PA among HCPs involved in RxTGA. Twenty-nine multi-disciplinary HCPs (Mage = 37.34; 75.9% female) completed a demographic questionnaire (e.g., age, years in profession, patients per week) and a validated 17-item-COM-B-survey to assess PA prescribing behaviour. Significant moderate correlations were found between Capability and Motivation (rs(29) = .501, p = .006), and Capability and Opportunity (rs(29) = .692, p < .001), in addition to a significant large correlation between Motivation and Opportunity (rs(29) = .727, p < .001). Results indicate that bidirectional relationships exist between a HCPs’ perceived capability to prescribe PA, motivation to prescribe, and opportunities to prescribe. Taken together, addressing these constructs appears to be important when designing and implementing PA prescription programs, like RxTGA, to optimize participant engagement. In doing so, positive benefits at the individual and societal level may emerge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".