Bibliographic record
Abstract
Although this conceptual framework is easy to understand, the data requirements for the mathematical model that underpins the framework are complex and must be defined carefully. In our framework, simulations of the health system are used to provide needs-based estimates that are aimed at optimizing outcomes. This type of model builds on research conducted at the macro, meso, and micro levels in order to reflect the complexity of relationships in the health human resource process. The papers in this issue of the Journal provide insight into specific constructs of the model. At the macro level, Tomblin Murphy explores methodological challenges in HHRP research. She examines common assumptions and the validity of their use in modelling in all aspects of the proposed framework. Tourangeau and colleagues report on the impact of hospital nurse-staffing decisions on 30-day mortality rates. Their model adds to our knowledge of the relationships among the management, deployment, and utilization of nursing services and patient/population outcomes. Shamian and colleagues explore the relationship between hospital-level indicators of the work environment and aggregated indicators of health and well-being for registered nurses employed in acute-care hospitals in the province of Ontario. This paper contributes to our understanding of how management decisions regarding the work environment influence nurse outcomes. Manojlovich and Ketafian explore the conflict between the practice of nursing and the organizational structure of many hospitals. This study provides insight into the management aspects of how the work unit is organized and the process of care delivery. Zboril-Benson examines the reasons for nurse absenteeism in the province of Saskatchewan. Birch describes the need for the planning process to take into account demographic changes in both populations and provider groups. A major challenge in modelling health human resources is access to meaningful databases for planning purposes. Pringle describes a unique Ontario initiative currently underway to develop and validate a nurse-sensitive set of data that will be routinely collected and will enhance HHRP in that province. Since the science that underpins HHRP is complex and rapidly changing, few books have been written on the subject. Reflecting the dynamic nature of the science, Tomblin Murphy and Barrath provide an excellent review of "grey literature" and useful Web sites for those interested in HHRP.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.095 | 0.059 |
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".