Developing human capital: what is the impact on nurse turnover?
Bibliographic record
Abstract
AIM: To investigate the impact that increasing human capital through staff training makes on the voluntary turnover of registered nurses. BACKGROUND: Healthcare organizations in Canada, the United Kingdom, the United States, and Australia are experiencing turbulent nursing labour markets characterized by extreme staff shortages and high levels of turnover. Organizations that invest in the development of their nursing human resources may be able to mitigate high turnover through the creation of conditions that more effectively develop and utilize their existing human capital. METHODS: A questionnaire was sent to the chief nursing officers of 2208 hospitals and long-term care facilities in every province and territory of Canada yielding a response rate of 32.3%. The analysis featured a three-step hierarchical regression with two sets of control variables. RESULTS: After controlling for establishment demographics and local labour market conditions, perceptions of nursing human capital and the level of staff training provided were modestly associated with lower levels of establishment turnover. CONCLUSIONS: and implications for Nursing Management The results suggest that healthcare organizations that have made greater investments in their nursing human capital are more likely to demonstrate lower levels of turnover of their registered nursing personnel.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".