Bibliographic record
Abstract
Supply-demand gap analysis is an essential part of planning for the medical workforce and services. Internal medicine is a specialty that is dedicated to providing primary and specialty care to adults. General internists provide medical care in hospital and community-based settings and perform academic activities such as research and teaching. Close to 40% of Canada’s population resides in Ontario. Ontario’s population has an increasing trend with seniors being the fastest growing age-group. Therefore, demand for general internists is rising rapidly in the province. A detailed study of the supply and demand systems and model driven analysis is needed for a future-proof planning of the supply of general internists. This doctoral research is undertaken with the motivation of providing decision makers with a simulation tool which will give both a micro-level and bird’s-eye view of the system and act as an aid in investigating policy options. This dissertation presents a system dynamics based ‘what-if scenario’ analysis tool for workforce planning of general internists in Ontario. Based on census data for IM residency, physicians in Ontario reports and Internal Medicine (IM) service utilization data in multiple healthcare settings in Ontario, a system dynamics-based supply model an analytical demand forecasting model is developed to estimate the supply-demand gap for general internists. The main contribution of this research is the availability of multiple practical levers within the model to simulate potential policies and evaluate their impact on the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".