Does the Availability of Hospital Beds Affect Utilization Patterns? The Case of End-of-Life Care
Bibliographic record
Abstract
Hospital downsizing in Canada during the 1990s raised public concern over the availability of hospital care, in addition to heightening administrative interest in improving or maximizing hospital utilization. One ongoing concern about hospital utilization is that a disproportionately large share of hospital resources is used by terminally ill and dying people. A research study using 1992/1993–1996/1997 in-patient abstracts data for the province of Alberta, Canada, was undertaken to explore and describe hospital utilization by dying in-patients. This investigation found only 48.2% of all deaths in Alberta over the five years studied involved hospital in-patients. An 18.5% reduction in the number of in-patient deaths and an 83.3% reduction in length of final stay occurred when 50% of acute care beds were closed, which was followed by an increase when beds began reopening — in terms of both the number of in-patient deaths (4.8%) and the average length of stay (2.6%). The ratio of men to women, the average age of dying in-patients, and the intensity of hospital care changed relatively little over those five years. Most in-patients were admitted for nursing care; in 51.3% of all cases, no diagnostic or therapeutic procedures were performed prior to death. These findings indicate hospital bed availability influences admission to hospital and length of stay, but not treatment decisions affecting seriously ill and dying patients. In addition, reduced length of stay appears to have been a widespread response to hospital downsizing, with this change substantially preserving individual access to hospitals.
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.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".