EXPLORING THE PERSPECTIVES OF NURSING STUDENTS ABOUT WORKING IN THE LONG-TERM CARE SECTOR
Bibliographic record
Abstract
Abstract Background The purpose of this study was to understand the perceptions of prospective registered nurses about working in the long-term care sector and identify workplace attributes that attract prospective nurses to the sector. Methods A sequential mixed methods study was conducted with nursing students at Ontario Tech University. Focus groups (n=14) asked students to comment on views about working in the long-term care sector, and job attributes that may attract them to the sector. A cross-sectional survey asked students to respond to elicited choice job scenarios, informed by the thematic analysis of focus group interviews. The survey data (n=139) was analysed using least absolute deviations estimator. Willingness-to-pay (WTP) values of wages gained or forgone were generated for each job attribute. Results Focus group interviews suggested fair compensation, optimal client-to-staff ratios, unionized work environments, comprehensive benefits packages, and flexible work arrangements were important job attributes. In survey results, 18.0% expressed interest in working in long-term care sector compared to 75.5% in acute care. Regression analysis suggested that higher amounts of paid vacation (WTP: -5.983; 95% CI: -13.749, -0.037) and higher risk of injury (WTP: 0.684; 95% CI: 0.124, 1.208) were associated with work in the long-term care sector. Conclusion Long-term care can attract nurses by offering flexible options such as part-time positions and paid vacation, and by actions that potentially mitigate the risk of workplace injury, violence, and abuse. Nursing students should be shown the positive aspects of working with older adults and dispel negative perceptions about the long-term care sector.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".