Physical activity assessment and counseling in Quebec family medicine groups.
Bibliographic record
Abstract
OBJECTIVE: To determine how often primary health care providers (PHCPs) in family medicine groups (FMGs) assess physical activity (PA) levels, provide PA counseling (PAC), and refer patients to exercise professionals; to describe patients' PA levels, physical fitness, and satisfaction regarding their PA management in FMGs; to describe available PA materials in FMGs and PHCPs' PAC self-efficacy and PA knowledge; and to identify characteristics of patients and PHCPs that determine the assessment of PA and PAC provided by PHCPs. DESIGN: Cross-sectional study using questionnaires and a medical chart audit. SETTING: Ten FMGs within the Integrated University Health Network of the Centre hospitalier universitaire de Sherbrooke in Quebec. PARTICIPANTS: Forty FPs, 24 nurses, and 439 patients. MAIN OUTCOME MEASURES: Assessment of PA level and PAC provided by PHCPs. RESULTS: Overall, 51.9% of the patients had had their PA level assessed during the past 18 months, but only 21.6% received PAC from at least 1 of the PHCPs. Similar percentages were found among the inactive (n = 244) and more active (n = 195) patients. The median PAC self-efficacy score of PHCPs was 70.2% (interquartile range 52.0% to 84.7%) and the median PA knowledge score was 45.8% (interquartile range 41.7% to 54.2%), with no significant differences between nurses and FPs. In multivariate analysis, 34% of the variance in PAC provided was explained by assessment of PA level, overweight or obese status, type 2 diabetes or prediabetes, less FP experience, lower patient annual family income, more nurse encounters, and a higher patient physical component summary of quality of life. CONCLUSION: The rates of assessment of PA and provision of PAC in Quebec FMGs were low, even though most of the patients were inactive. Initiatives to support PHCPs and more resources to assess PA levels and provide PAC should be implemented.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".