AN ANALYTICAL FRAMEWORK FOR OPTIMAL PLANNING OF LONG-TERM CARE FACILITIES IN ONTARIO
Bibliographic record
Abstract
Long-term care facility network in Ontario, and in Canada as a whole, encounters critical issues regarding balancing demand with capacity. Even worse, it is faced with rising demand in the coming years. Moreover, there is an urgent need to provide long-term care for patients in their own language (particularly French). This study proposes a dynamic Mixed-Integer Linear Programming model based on the current standing of the long-term care system in Ontario, which simultaneously optimizes the time and location of constructing new long-term care facilities, adjusting the capacity (namely, human resources and beds) of each facility dynamically, and the assignment of patients to the facilities based on their demand region, gender, language, and age group over a finite time horizon. We apply the diversity-support constraints, based on patients’ gender and language, to save patients from loneliness and to comply with the Canadian values of providing care. Finally, we validate the model by performing a case study in Hamilton, Ontario. An extensive set of numerical analyses are explored to provide deeper insights into the whole issue. One set of such analysis is an extensive simulation study to examine the effect of distributional uncertainty in some of the input parameters on the optimal results, hence providing a much more realistic understanding of the optimization model.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".