The true complexities of “standard” family practice visits unmasked: an observational cross-sectional study in Regina
Bibliographic record
Abstract
BACKGROUND: Many patients present to their family medicine clinic with more than one health concern, placing an increased demand on family physicians. Research into the average number of concerns per regular family medicine visit is limited. Recognition of the frequency that family physicians address more than one concern per visit and adapting practices accordingly is important for improving patient care. OBJECTIVE: To examine whether family physicians routinely address multiple different patient concerns during a single visit and if this is influenced by patient demographics. METHODS: This study was conducted at a multi-physician family medicine clinic in Regina, Saskatchewan, Canada. Five physicians contributed their 500 most recent charts, extending retrospectively from 1 June 2023, from in-person visits by patients over 18 years of age and billed as regular appointments without billed procedures. Each chart was reviewed for the number of concerns addressed in the visit. RESULTS: Fifty percent of visits addressed more than 1 concern (range = 1-8). A generalized linear mixed model using Poisson distribution showed certain physicians (incident rate ratio [IRR]: 1.192, 95% CI: 1.087-1.307, P < 0.001) and adults older than 65 years compared to adults less than 40 years (IRR 1.151, 95% CI: 1.069-1.239, P < 0.001) were more likely to present with multiple concerns, but patient sex was not a significant predictor. CONCLUSIONS: Family physicians routinely address more than one concern per visit. Standard visit length and billing practices should be adapted to reflect this complexity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".