Methodological issues in health human resource planning: cataloguing assumptions and controlling for variables in needs-based modelling.
Bibliographic record
Abstract
Health Human Resource Planning (HHRP) models approximate future nursing requirements based on a variety of factors specific to the model being employed. There is an urgent need to develop a better understanding of the sources of bias in statistical modelling in order to ensure that we are guided by accurate and robust formulae. This paper addresses these issues as they apply in the context of needs-based HHRP research for nursing by presenting a review and discussion of the relevant literature as it relates to: (1) the testing of assumptions, (2) avoiding ecological and atomistic fallacies, (3) how need is directly or indirectly related to health care, and (4) alternatives to aggregate analysis for assessing the relationship between health needs and utilization of nursing services. The paper concludes that multilevel modelling is useful for the simulation analysis of individuals and their ecologies, and that small area variation modelling holds promise for assessing the relationship between health needs and utilization of nursing services.
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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 | 0.139 | 0.376 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".