“Working Against Gravity”: The Uphill Task of Overcapacity Management
Bibliographic record
Abstract
While most health systems have implemented interventions to manage situations in which patient demand exceeds capacity, little is known about the long-term sustainability or effectiveness of such interventions. A large multi-jurisdictional study on patient flow in Western Canada provided the opportunity to explore experiences with overcapacity management strategies across 10 diverse health regions. Four categories of interventions were employed by all or most regions: overcapacity protocols, alternative locations for emergency patients, locations for discharge-ready inpatients, and meetings to guide redistribution of patients. Two mechanisms undergirded successful interventions: providing a capacity buffer and promoting action by inpatient units by increasing staff accountability and/or solidarity. Participants reported that interventions demanded significant time and resources and the ongoing active involvement of middle and senior management. Furthermore, although most participants characterized overcapacity management practices as effective, this effectiveness was almost universally experienced as temporary. Many regions described a context of chronic overcapacity, which persisted despite continued intervention. Processes designed to manage short-term surges in demand cannot rectify a long-term mismatch between capacity and demand; solutions at the level of system redesign are needed.
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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 | 0.028 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.034 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".