A patient-oriented approach to the development of a primary care physical activity screen for embedding into electronic medical records
Bibliographic record
Abstract
Physical activity questionnaires exist, but effective implementation in primary care remains an issue. We sought to develop a physical activity screen (PAS) for electronic medical record (EMR) integration by 1) identifying healthcare professionals’ (HCPs), patients’ and stakeholders’ barriers to and preferences for physical activity counselling in primary care; and 2) using the information to co-create the PAS. We conducted semi-structured interviews with primary care HCPs, patients and stakeholders, and used content and thematic analyses to inform iterative co-design of the PAS. Interviews with 38 participants (mean age 41 years) resulted in 2 themes: 1) HCPs are willing to conduct physical activity screening, but acknowledge they don’t do it well; and 2) HCPs have limited opportunity and capacity to discuss physical activity, and need a streamlined process for EMR that goes beyond quantifying physical activity. HCPs, patients and stakeholders co-designed a physical activity screen for integration into the EMR that can be tested for feasibility and effects on HCP behaviour and patients’ physical activity levels. Novelty: EMR-integration of physical activity screening needs to go beyond just asking about physical activity minutes. Primary care professionals have variable knowledge and time, and need physical activity counselling prompts and resources. We co-developed a physical activity EMR tool with patients and primary care providers.
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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 | 0.099 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".