The effect of medical work groups on hospital resource use
Bibliographic record
Abstract
This study tests the ability of medical work groups to overcome coordination problems related to group decision-making in allocating clinical resources to inpatients. The study was conducted over a 32-month period in two medium-sized acute-care hospitals located in Montreal, Quebec, Canada. The data were collected by hand from the medical charts of 10,456 patients in the surgical and medical departments. The Linear Structural Relations (LISREL) approach was employed to address the work-group issue using a task contingent model of work-group organization. In this model, the nature of the task is fundamental because its level of complexity determines both the organization of the work group and the use of resources. Medical work-group mechanisms should be efficient to the extent that resource utilization is explained solely by task characteristics rather than by work-group structure. In this study, the following two major organizational concepts were used as factors to explain resource use: task characteristics and work-group characteristics. Our analysis confirmed the main points of the task contingency theory as applied to the field of medicine. First, the results confirm that resource utilization is explained mainly by task complexity. Second, they confirm that medical work groups modulate their structures on the basis of task characteristics and do not explain resource use. The results also reveal a more complex model in which, for instance, the concepts of medical task and medical professional work are not easy to separate. The results highlight the interest in conceptualizing and analysing medical practice in work groups. It raises important issues that have seldom been taken into account in the study of medical practice variations, which has tended to focus on attending physicians.
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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 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".