Examining Predictors of Intention to Leave in Home Care and Differences among Types of Providers
Bibliographic record
Abstract
The retention and recruitment of care providers are ongoing concerns in healthcare globally. Examining intention to leave (ITL) as a measure of retention, the existing literature has focused on nurses working in hospitals, with less attention paid to other care providers and other areas of practice. The purpose of this study was to gain a better understanding of the unique factors influencing ITL among three categories of care providers in home care: registered practical nurses, registered nurses, and personal support workers. This study assessed and compared predictors of ITL, including organizational commitment, job satisfaction, perceived supervisor support, burnout, role stress, work/family conflict, and community satisfaction. A convenience sample of home care staff working in one agency in a Canadian province was sent an electronic survey by e-mail in 2021. Responses (n = 185) underwent data analysis including descriptive statistics, analysis of variance, and multiple linear regression, as well as thematic analysis of two open-ended items. The results of the study indicated that 54% (n = 99) of respondents were considering leaving their job, and respondents were dissatisfied with their salary and benefits. Role stress, work-family conflict, and burnout differed significantly between groups. Several themes emerged for strategies to promote employees to stay with the agency, with the overwhelming strategy being higher wages/salary. Themes for why employees stayed with the agency included love for clients and commitment to their care, as well as fondness for the teams within which respondents worked. The findings of the study lead to several important implications and recommendations for the home care sector. Advocating for wage parity among healthcare sectors and other opportunities for compensation for home care workers is necessary. Additional strategies include supportive and innovative approaches for scheduling, teamwork, and working with staff to identify barriers and solutions in home care.
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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 | 0.003 | 0.012 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".