Scheduling of Nurses at a Community Health Care Centre
Bibliographic record
Abstract
This project looks at optimizing nurse schedules at a community health centrein Vancouver, Canada. The centre provides primary care to individuals who areunable to access conventional fee-for-service primary care. Nurses at the Centrehave reported that their schedules do a poor job of matching patient arrival levelsto the walk-in components of the clinic. This paper describes patient flow throughspeci1c components of the centre as a scheduling problem in the form of a MixedInteger Linear Program (MILP). Having collaborated with the centre’s management,we built diagrams outlining the flow and used electronic medical record (EMR) dataand staff opinion to estimate the parameters needed in the model. The model iswritten in Python and solved using Gurobi to approximate the optimal schedulefor nurses to maximize the amount of time spent with patients. The centre hasexpressed interest in implementing the generated schedules for nurses.
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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 | 0.001 | 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 | 0.001 | 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) | 0.002 | 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 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".