Case Article—Pediatrician Scheduling at British Columbia Women’s Hospital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article describes an in-class role-playing exercise, as well as a case study, on the application of mixed integer programming to help a hospital with physician scheduling. The intended audiences are graduate students or advanced undergraduate students taking a first course in optimization who have been introduced to integer programming. The role-playing exercise aims to develop students’ skills in the iterative process of listening to decision makers describe their problem, asking them questions, and developing initial formulations of the problem. The case study provides students the opportunity to spend more time developing a full mathematical formulation, solving it, and writing up their findings. The case assumes students have already been introduced to the “Big-M” method but assumes no prior introduction to the concepts of hard versus soft constraints. There is no natural objective in this problem, such as the usual “maximize profit” or “minimize cost”; instead, students are introduced to the topic of Goal Programming, which also introduces them to the concept of multiobjective optimization. Supplemental Material: Data are available at https://doi.org/10.1287/ited.2021.0266ca . The Teaching Note is available at https://www.informs.org/Publications/ Subscribe/Access-Restricted-Materials .
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 it