Team-based Staffing Optimization for an Urgent and Primary Care Centre
Bibliographic record
Abstract
Urgent and primary care centres (UPCCs) provide both walk-in services for urgent healthcare needs and booked appointments for longitudinal care. UPCCs utilize multi-disciplinary teams of healthcare professionals who collaborate to provide client care. This thesis develops a new approach to optimize team-based staffing at a UPCC in Vancouver, British Columbia. The core of the approach is a discrete event simulation that estimates client access indicators based on the UPCC operational profile and client visit data. The analysis compares two algorithms that minimize staffing levels subject to access targets given by the time-dependent expected proportion of simulated clients who leave due to a prolonged wait. One approach combines an extension of an iterative, simulation-based algorithm for small-interval staffing with an integer programming formulation for shift-based staffing. Another approach optimizes shift-based staffing through simulation optimization. Both approaches make staffing recommendations to improve care access.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 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) | 0.001 | 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 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".