Workload and patterns of care in the Timmins Family Health Team in Ontario
Bibliographic record
Abstract
OBJECTIVE: To characterize primary care physician and nurse practitioner ("GP") workload and availability, and any relationship with daytime, low-acuity emergency department (ED) and after-hours walk-in clinic (WIC) visit counts. DESIGN: Retrospective database review. SETTING: Timmins, Ont, with 5 family health team (FHT) office sites, 1 after-hours FHT WIC, and 1 ED. PARTICIPANTS: An anonymous data set representing 21 voluntarily enrolled GPs comprising 33 211 office appointments among 15 908 patients, plus 2043 ED visits and 2713 WIC visits, over 18 months. MAIN OUTCOME MEASURES: was defined as the corrected number of office visits per patient per year. Linear and nonlinear relationships between GP availability and each roster's chronic disease burden (congestive heart failure, chronic obstructive pulmonary disease, and diabetes); ED visit count per patient; and WIC visit count per patient. RESULTS: Corrections for dormant patients and then for each of relative nursing support and patient complexity changed roster sizes by a mean (SD) of -8.4% (14.5%), -7.1% to 5.6% (median -1.6%), and 32.0% (18.2%), respectively. Combining these corrections increased effective roster size by a mean (SD) of 18.4% (7.3%). Larger rosters were not proportionately more dormant. In the Timmins FHT, GPs saw unique patients about 2.05 times per year (range 1.39 to 3.81). Availability of GPs did not change with increasing numbers of patients with congestive heart failure, chronic obstructive pulmonary disease, or diabetes in the roster. The ED diversion model had low explanatory power and was likely unreliable. The WIC diversion model was more robust, predicting 0.08 fewer WIC visits per patient per year if GP availability increased from 2.0 to 3.0 visits per patient per year (relative risk reduction of 41%). CONCLUSION: Sampled GPs manage a more complex patient population on average than their uncorrected roster sizes imply. There was no evidence that larger rosters or those with more patients with comorbid conditions reduced GP availability. Increasing physician availability might decrease WIC attendance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".