The Cost of Integrating a Physical Activity Counselor in the Primary Health Care Team
Bibliographic record
Abstract
OBJECTIVE: This article assesses direct costs of integrating a physical activity counselor (PAC) into primary health care teams to improve physical activity levels of inactive patients. METHODS: A monthly cost analysis was conducted using data from 120 inactive patients, aged 18 to 69 years, who were recruited from a community-based family medicine practice. Relevant cost items for the intensive counseling group included (1) office expenses; (2) equipment purchases; (3) operating costs; (4) costs of training the PAC; and (5) labor costs. Physical and human capital were amortized over a 5-year horizon at a discount rate of 5%. RESULTS: Integrating a PAC into the primary health care team incurred an estimated one-time cost of CA$91.43 per participant per month. Results were very sensitive to the number of patients counseled. CONCLUSIONS: The costs associated with the intervention are lower than many other intervention studies attempting to improve population physical activity levels. Demonstrating this competitive cost base should encourage additional research to assess the effectiveness of integrating a PAC into primary health care teams to promote active living among patients who do not meet recommended physical activity levels.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".