Nurses’ Expert Opinions of Workplace Interventions for a Healthy Working Environment: A Delphi Survey
Bibliographic record
Abstract
Much has been written about interventions to improve the nursing work environment, yet little is known about their effectiveness. A Delphi survey of nurse experts was conducted to explore perceptions about workplace interventions in terms of feasibility and likelihood of positive impact on nurse outcomes such as job satisfaction and nurse retention. The interventions that received the highest ratings for likelihood of positive impact included: bedside handover to improve communication at shift report and promote patient-centred care; training program for nurses in dealing with violent or aggressive behaviour; development of charge nurse leadership team; training program focused on creating peer-supportive atmospheres and group cohesion; and schedule that recognizes work balance and family demands. The overall findings are consistent with the literature that highlights the importance of communication and teamwork, nurse health and safety, staffing and scheduling practices, professional development and leadership and mentorship. Nursing researchers and decision-makers should work in collaboration to implement and evaluate interventions for promoting practice environments characterized by effective communication and teamwork, professional growth and adequate support for the health and well-being of nurses.
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.
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 | 0.035 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".