Recruitment and Retention of Nurses: Challenges Facing Hospital and Community Employers
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
Understanding nurses' perceptions of their workplaces underpins successful recruitment and retention initiatives, particularly in this time of global nursing shortage. The American Nurses Association and the American Academy of Nursing have identified "magnet characteristics"--organizational factors that support excellent practice and working conditions in hospital settings. Using selected magnet characteristics, this exploratory study examined nurses' perceptions of their work experiences in both hospital and community settings. Mail surveys were completed by community and hospital nurses (n = 1248) selected randomly from a provincial registry in Ontario, Canada. Scales measured organizational factors (organizational and immediate supervisor support, decentralized decision-making, nurse-physician relationships and work-group cohesiveness) and job-related factors (autonomy, job challenge, work demands, fair treatment, work-status congruence; satisfaction with career, salary, working conditions) of nurses' experiences in their work settings. Nurses in both sectors wanted more opportunities to participate in decision-making and recognition for their contributions to their organizations. In the hospital sector, nurses reported significantly lower levels of perceived organizational and supervisory support and autonomy, and were less satisfied with working conditions and scheduling. Nurses in the community sector were most dissatisfied with salary. No cross-sector differences were reported on nurse-physician relationships, degree of job challenge or career satisfaction. Successful recruitment and retention initiatives hinge on the ability (and willingness) of healthcare organizations to attend to the concerns expressed by nurses and create work settings that are attractive to both new recruits and nurses currently in their employ.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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