Evaluating the effectiveness of physician counseling to promote physical activity in Mexico: an effectiveness-implementation hybrid study
Bibliographic record
Abstract
Integrating physical activity (PA) counseling in routine clinical practice remains a challenge. The purpose of this study was to evaluate the implementation and effectiveness of a pragmatic strategy aimed to improve physician PA counseling and patient PA. An effectiveness-implementation type-2 hybrid design was used to evaluate a 3-h training (i.e., implementation strategy-IS) to increase physician use of the 5-As (assess, advise, agree, assist, arrange) for PA counseling (i.e., clinical intervention-CI) and to determine if the CI improved patient PA. Patients of trained and untrained physicians reported on PA and quality of life pre-post intervention. Medical charts (N = 1700) were examined to assess the proportion of trained physicians that used the 5-As. The RE-AIM framework informed our evaluation. 305/322 of eligible physicians participated in the IS (M age = 40 years, 52% women) and 683/730 of eligible patients in the CI (M age = 49 years, 77% women). The IS was adopted by all state regions and cost ~ $20 Mexican pesos (US$1) per provider trained. Physician adoption of any of the 5-As improved from pre- to post-training (43 vs. 52%, p < .01), with significant increases in the use of assessment (43 vs. 52%), advising (25 vs. 39%), and assisting with barrier resolution (7 vs. 15%), but not in collaborative goal setting (13 vs. 17%) or arranging for follow-up (1 vs. 1%). Patient PA and quality of life did not improve. The IS intervention was delivered with high fidelity at a low cost, but appears to be insufficient to lead to broad adoption of the CI.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".