Beyond demographic change in human resources planning: an extended framework and application to nursing
Bibliographic record
Abstract
OBJECTIVES: To introduce health care production functions into human resources planning and to apply the approach to analysing the need for registered nurses in Ontario during a period of major reduction in inpatient capacity. METHODS: Measurement of changes in services delivered by acute care hospitals in Ontario between 1994/95 and 1998/99, and comparison with changes in the mix of human resources, non-human resources and patient needs. RESULTS: Inpatient episodes per nurse fell by almost 2%. At the same time the number of beds was cut by over 20%. As a result, the number of patients per bed increased by 12%. Allowing for severity, there was a 20% reduction in beds per episode and a 3.7% reduction in nurses per episode. CONCLUSIONS: The demands on nurses in acute care hospitals have increased as an increasing number of severity-adjusted episodes are served using fewer beds by a reduced number of nurses. Human resources planning traditionally only considers the effects of demographic change on the need for and supply of health care. Failure to recognize the variable and endogenous nature of other health care inputs leads to false impressions about the adequacy of existing supplies of human resources. Consideration of human resources in the context of the production function for health services provides a meaningful way of improving the effectiveness and efficiency of human resources planning.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".