Forecasting models for human resources in health care
Bibliographic record
Abstract
This article is a review of the approaches published between 1996 and 1999 that have been used to forecast human resource requirements for nursing. Much of the work to date generally does not consider the complex factors that influence health human resources (HHR). They also do not consider the effect of HHR decisions on population health, provider outcomes such as stress, and the cost of a decision made. Supply and demand approaches have dominated. Forecasting is limited, too, by the availability of reliable and valid data bases for examining supply and use of nursing personnel across sectors. Three models--needs based, utilization based, and effective demand based--provide substantially different estimates of future HHR need. The methods of analysis employed for forecasting range from descriptive to predictive and are borrowed from demography, epidemiology, economics, and industrial engineering. Simulation models offer the most promise for the future. The forecasting methods described have demonstrated their accuracy and usefulness for specific situations, but none has proven accurate for long-term forecasting or for estimating needs for large geographical areas or populations.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".