Work-related factors influencing home care nurse intent to remain employed
Bibliographic record
Abstract
BACKGROUND: Health care is shifting out of hospitals into community settings. In Ontario, Canada, home care organizations continue to experience challenges recruiting and retaining nurses. However, factors influencing home care nurse retention that can be modified remain largely unexplored. Several groups of factors have been identified as influencing home care nurse intent to remain employed including job characteristics, work structures, relationships and communication, work environment, responses to work, and conditions of employment. PURPOSE: The aim of this study was to test and refine a model that identifies which factors are related to home care nurse intentions to remain employed for the next 5 years with their current home care employer organization. METHODOLOGY/APPROACH: A cross-sectional survey design was implemented to test and refine a hypothesized model of home care nurse intent to remain employed. Logistic regression was used to determine which factors influence home care nurse intent to remain employed. FINDINGS: Home care nurse intent to remain employed for the next 5 years was associated with increasing age, higher nurse-evaluated quality of care, having greater variety of patients, experiencing greater meaningfulness of work, having greater income stability, having greater continuity of client care, experiencing more positive relationships with supervisors, experiencing higher work-life balance, and being more satisfied with salary and benefits. PRACTICE IMPLICATIONS: Home care organizations can promote home care nurse intent to remain employed by (a) ensuring nurses have adequate training and resources to provide quality client care, (b) improving employment conditions to increase income stability and satisfaction with pay and benefits,
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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 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".