Encouraging Obstetrics and Gynecology Residents to Deliver Cost-Conscious Care: A Randomised Controlled Trial [4I]
Bibliographic record
Abstract
INTRODUCTION: Residents have a professional obligation to the stewardship of healthcare resources yet there is a paucity of research on how to improve their cost-awareness. Rising health care expenditure has highlighted a critical need to improve education in this competency. This study aims to test if an educational module can teach residents to make cost-conscious management plans and reduce health care spending. METHODS: All Canadian Obstetrics and Gynecology residents in 2017 were eligible for this randomised controlled trial. Institutional ethics board approval was obtained. The study was administered online via REDCap. Interested residents were enrolled, stratified by level of training and block randomised. Residents completed a survey to determine their management of four obstetrical scenarios. The intervention group reviewed an educational module on cost-effective ordering prior to completing the survey; the control group had the option to review it after. The primary outcome was mean total expenditure as calculated from the survey. Student t-test was used to compare the mean total expenditure between the two groups. RESULTS: 85 residents were enrolled, 63 residents completed study requirements (30 intervention and 33 control). Mean total expenditure was $291.03 CAD (95% confidence interval [CI] 259.38-322.68) versus $192.98 CAD (95% CI 170.67-215.29) in the control and intervention groups respectively, corresponding to a 33.69% or $98.05 CAD (P=.0001) reduction in total expenditure. CONCLUSION: This educational module decreased expenditure by Canadian Obstetrics and Gynecology residents in the management of hypothetical obstetrical cases. This introduces a potential curriculum innovation to improve resident education in judicious use of healthcare resources.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".