Factors associated with recording the exercise vital sign (EVS) in the electronic health records of patients in chiropractic teaching clinics.
Bibliographic record
Abstract
Objective: We set out to identify factors associated with recording of exercise minutes per week in electronic patient files at chiropractic teaching clinics to better understand whether this important health determinant - exercise vital sign (EVS) - is captured or not. Methods: Patient files (4018) from 23 clinicians eligible for inclusion underwent multilevel logistic regression modeling to explore the association between a recorded EVS and the following: patients' age, sex, comorbidities and interns nested within clinicians. Results: EVS discussion was documented in 81.2% of patient files, whereas 44.9% had exercise minutes recorded numerically. Clinicians and interns explained 1.7% and 25.5% of the variance in the EVS outcome. Conclusion: To enhance EVS recording, clinic directors and clinicians should better educate the interns on the importance of exercise is medicine and appropriate record keeping, as they explained the largest portion of variability in recording exercise in minutes per week.
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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 | 0.003 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".