Evaluating the Effectiveness of a Physical Activity Referral Scheme Among Women
Bibliographic record
Abstract
Evidence supports the effectiveness of interventions delivered in primary care to promote physical activity (PA). Specifically, approaches where physician counseling is coupled with other strategies (eg, referrals to community resources) have been recognized as the most promising. The purpose of this study was to compare the effectiveness of a PA prescription plus referral intervention versus a prescription only intervention delivered in primary care. Ten family physicians and their female patients (N = 35, mean age = 36 years) were randomly assigned to 1 of 3 conditions: prescription plus (n = 12), prescription only (n = 12), and usual care (n = 11). The prescription plus group received a PA prescription plus a referral to a community program, the prescription only group received only the PA prescription, and the usual care group received usual health care. The Godin Leisure-Time Exercise Questionnaire was used to measure PA. A significant increase on the PA score (P < .05, partial η(2) = .178) and on total weekly PA minutes (P < .05, partial η(2) = .179) was observed in both prescription groups after the intervention. There were no significant group differences (P > .05). No PA changes were observed in the usual care group. Findings from this pilot study suggest that brief PA counseling and a prescription delivered in primary care can be effective for promoting PA among women. Referring patients did not seem to enhance the effect on PA.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 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".