Physicians promoting physical activity using pedometers and community partnerships: a real world trial
Bibliographic record
Abstract
BACKGROUND: Healthcare providers are a primary source of information on preventive health issues for patients. Further research related to physical activity (PA) promotion in the primary care setting is needed. OBJECTIVE: To explore the feasibility of a physician and community PA intervention using a pedometer for: increasing PA levels among inactive patients; enhancing relationships between physicians and community PA partners; and influencing the PA promotion habits of physicians. METHODS: A 6-week PA intervention was delivered to 152 inactive patients who were recruited in physician waiting rooms, counselled by their physicians, provided with a pedometer and referred to a community action site (CAS) at their local recreation centre for further support. PA was measured at baseline and follow-up using the International Physical Activity Questionnaire. Follow-up interviews were conducted with physicians and recreation representatives on the challenges and benefits associated with the intervention. RESULTS: Study patients significantly increased their PA (103% change in MET min/week). Physicians and recreation professionals were highly satisfied with the intervention and partnership. There were challenges to ongoing communication. Physicians reported increased awareness of the pedometer and community resources as supports for PA. CONCLUSION: A pedometer-based PA intervention delivered by physicians in partnership with a community PA stakeholder is feasible and warrants further research. Long-term maintenance of this intervention would require resources for pedometers, and ongoing contact and communication between the physicians and CAS to ensure availability of community supports.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".