What Is the “Right” Number of Hospital Beds for Palliative Population Health Needs?
Bibliographic record
Abstract
Healthcare services are one of the twelve determinants of population health. While all types of healthcare services are important, timely access to hospital-based care when needed is critical. For three decades, long waits and wait lists for hospital admission and inpatient care have been a concern in Canada. Undersupply of hospital beds to meet population needs may be the cause of this as hospitals were downsized due to government funding cutbacks and hospital expansion has not occurred since despite population growth and aging. The availability of hospital beds for palliative population health needs may therefore be an issue, particularly as longstanding concern exists about terminally-ill and dying people being frequently admitted to hospital and having long hospital stays. A decline in hospital deaths in many developed countries, including Canada, could indicate that palliative population needs for hospital-based care are not being met. This paper compares the number of hospitals and hospital beds that exist in 9 Canadian provinces and 15 developed countries in relation to population and spatial considerations in an attempt to determine an optimal number of hospital beds for the general public and thus also palliative population health needs. Methods: Document analysis. Publicly-available hospital, population, and geographic information was sought for 9 Canadian provinces and 15 developed countries and compared. Results: Major differences in citizen to hospital bed ratios and citizen to hospital ratios across provinces and countries were found. The availability of hospitals and hospital beds clearly varies. Conclusion: Some regions may have too few hospitals and hospital beds to meet the palliative and other care needs of their citizens. Sufficient beds should exist so necessary admissions to hospital can occur without harmful delay.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".