Recruitment and Retention of Nurses: Challenges Facing Hospital and Community Employers
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.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".