How can we increase attraction and retention of nurses? A research with young nurses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The persistent challenges in attracting and retaining a diverse healthcare workforce, with a specific focus on nurses, have become increasingly pronounced in recent years. These hurdles have been exacerbated by a growing difficulty in retaining young nurses, thereby exacerbating labor shortages driven by demographic shifts and the retirement of experienced nursing professionals. While most research efforts have concentrated on the broader issue of nurse retention, our study is centered on a specific demographic—young nurses. Our research endeavors to shed light on the unique challenges faced by young nurses through a qualitative survey involving nursing students who are simultaneously employed. We seek to discern the multifaceted obstacles they encounter in both their academic environment and the healthcare organizations where they work. While certain challenges are linked to course organization, examinations, and the time required for studying, our respondents overwhelmingly emphasize the pivotal role of the work environment in facilitating the harmonization of work, family, and educational commitments. This reconciliation is achieved through measures such as flexible working arrangements and the efficient organization of nursing duties. The primary objective of our research is to provide insights into how these diverse challenges can be effectively addressed and how a range of measures can significantly contribute to the attraction and retention of nursing students, as well as the long-term retention of nurses within the healthcare system. Our recommendations are intended to be of practical use to a wide array of stakeholders, including academic institutions, particularly colleges and universities offering nursing programs, as well as hospitals, clinics, and other healthcare institutions that hire nurses. By collaboratively addressing these challenges and implementing the recommended measures, we aim to fortify the healthcare workforce and ensure the continued provision of quality care to patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it