The physical activity counselling (PAC) randomized controlled trial: rationale, methods, and interventions
Bibliographic record
Abstract
Primary care is a promising venue to build patient motivation and confidence to increase physical activity (PA). Physician PA counselling has demonstrated some success; however, maintenance of behaviour change appears to require more intensive interventions. In reality, most physicians do not have the necessary training nor the time for this type of counselling. The purpose of this paper is to outline the rationale, methods, and interventions for the ongoing physical activity counselling (PAC) randomized controlled trial (RCT), which aims to assess the impact of integrating a PA counsellor into a primary care practice. This RCT has 2 arms: (i) brief PA counselling (2-4 min) from a health care provider and (ii) brief PA counselling+intensive PA counselling from a PA counsellor (3 months). The impact of this intervention is being evaluated using the comprehensive RE-AIM framework. One hundred twenty insufficiently active adult patients, aged 18 to 69 y and recruited during regular primary care visits have been randomized. Dependent measures include psychological mediators, PA participation, quality of life, and physical and metabolic outcomes. The PAC project represents an innovative, theoretically-based approach to promoting PA in primary care, focusing on psychological mediators of change. We anticipate that key lessons from this study will be useful for shaping future public health interventions, theories, and research.
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".