Characteristics of the registered nurse workforce : associations with mortality rates in general and hospital-based populations
Bibliographic record
Abstract
Concerns regarding a shortage of registered nurses (RNs) have underscored the importance of improving methods for workforce planning. The goal of this dissertation was to contribute knowledge that could enhance nursing human resources (NHR) planning, with a particular focus on a population health, needs-based approach. This research relied on descriptive-exploratory analyses using repeated measures of data obtained from the College of Registered Nurses of British Columbia (BC) and publicly available reports of the BC Vital Statistics Agency and Canadian Institute for Health Information. Three studies were conducted to: a) investigate the spatial and temporal patterns and trends in the BC RN workforce (Study one); b) examine the associations between selected characteristics of the RN workforce and indicators of population health (Study two); and c) examine the associations between selected characteristics of the RN workforce and the hospital standardized mortality ratio (HSMR) (Study three). Small area analysis (Studies two and three) and mixed effects statistical models (Studies two and three) were used. The results of study one showed that geographic areas (BC’s local health areas [LHAs]) with low general population density (i.e., < 10,000 general population) differed from higher general population density areas in the patterns and trends of the selected RN workforce characteristics, both spatially and temporally. In study two, correlations between selected population health indicators and RN workforce characteristics were modest in magnitude and only a few of these correlations persisted in the three years of the study (2002 – 2004). No statistically significant relationships were found between the selected population health indicators and RN workforce characteristics. The findings of study three indicated that greater numbers of RN managers per 100 hospital beds were associated with lower HSMRs (lower hospital mortality). Findings from these studies suggest that geographic context at the small area level is an important consideration for NHR planning and that decision-makers need to look beyond the supply of RNs and examine how other workforce characteristics influence planning. Some of the limitations of currently available data and methods for planning NHR are identified, particularly related to needs-based planning, and avenues for further research regarding NHR planning are recommended.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".