Data-Centric System Dynamics Modelling of General Internal Medicine Physicians in Ontario: Forecasting Supply and Workload Analysis
Bibliographic record
Abstract
Internal Medicine and General Internal Medicine (IM/GIM) physicians play a critical role in delivering adult inpatient care across Ontario. In recent years, this workforce has experienced mounting pressure due to population aging and the lingering effects of the COVID-19 pandemic. This study develops a system dynamics model to project the supply of IM/GIM physicians in Ontario from 2009 to 2040. The model disaggregates addition and attrition flows, and is calibrated using 2009–2019 data, then validated against 2020–2023 data. Demand is estimated using Resource Intensity Weights (RIW), a standardized case-mix measure that captures both the volume and complexity of patient care, and physician workload, defined as the ratio of demand to supply, serves as the core performance indicator. The model demonstrates strong predictive accuracy in the calibration and validation phases. Forecasts indicate a declining workload trend until 2032, followed by a steady rise until 2040. A scenario analysis of 33 configurations reveals that moderate increases in residency quotas (30–40%) and external entry (30–50%) can substantially alleviate future workload rise, particularly when implemented proactively.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".