Physical Activity Assessment and Counseling
Bibliographic record
Abstract
PURPOSE: Primary health care providers (PHCP) are excellent resources to address the prevalent problem of inactivity in the population. Some studies provided information on physical activity counselling (PAC) in Canadian primary care contexts, but none used medical chart audits to collect data. They focused primarily on primary care physicians (PCPs), without including other PHCP. Thus, the purpose of this study was to i) quantify the assessment of PA level, PAC provided and referral to kinesiologists performed by PHCP of patients treated in Quebec Family Medicine Groups (FMGs); ii) identify the determinants of the assessment of PA level performed and PAC provided by PHCP. METHODS: An 18 months retrospective medical chart review was performed by trained research personnel using a standardized grid to obtain information about PHCP’s practice, the number of kinesiologist referrals and patients’ comorbidities. Sociodemographic data were self-declared in a questionnaire. Patients’ leisure PA was determined using the Canadian Community Health questionnaire and quality of life with the Short Form-36. RESULTS: Forty one PCPs (48±10 years, 63% of female), 24 nurses (36±12 years, 92% of female) and 439 patients (58±14 years, 66% of female, BMI 29±6 kg.m2, 56% of inactive) from 10 Quebec FMGs were recruited between 2009 and 2012. According to chart audits, PCPs referred 0.2 % of patients to a kinesiologist. Almost, 52% of patients had their PA level assessed during the last 18 months, but only 22% received PAC by one of the PHCP. More exactly, PCPs performed PAC with 15% of their patients and nurses with 18%. In multivariate analysis, 34% of the PAC’s performed by at least one PHCP variance was explained by : [OR (95%CI)] PA level assessment [4.32 (2.37-7.85)], overweight/obese status [3.21 (1.46-7.10)], type 2 Diabetes/Pre-Diabetes status [2.84 (1.51-5.37)], PHCPs experience [0.97 (0.94-0.99)], patient’s annual family income 55 000 $ [0.56 (0.32-0.97)], number of nurses’ encounters [1.22 (1.10-1.35)] and patient’s physical component of the quality of life [1.06 (1.03-1.10)]. CONCLUSION : Although the majority of patients were inactive, the assessment of PA and PAC is low in the Quebec FMGs. Initiatives to help PHCP and more resources to assess PA level and provide PAC should be favor in FMGs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".