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Record W2105402109 · doi:10.3122/jabfm.2012.02.110154

The Cost of Integrating a Physical Activity Counselor in the Primary Health Care Team

2012· article· en· W2105402109 on OpenAlexaff
William Hogg, Xiaolin Zhao, Derek C. Angus, Michelle Fortier, Judy Zhong, Tracey O’Sullivan, Ronald J. Sigal, Chris M. Blanchard

Bibliographic record

VenueThe Journal of the American Board of Family Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicinePrimary careIntervention (counseling)Health careFamily medicinePhysical activityTotal costPhysical therapyNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.378
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2012
Admission routes1
Has abstractyes

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