Bibliographic record
Abstract
OBJECTIVE: In this study nurses' views on work enabling factors, priority issues for improvement, and stress and satisfaction levels were examined. BACKGROUND: This research builds on previous work studies that empirically established work dimension indices related to individual and organizational outcomes. A survey tool was adapted so it replicated previous instrument development research, as well as explored its practicality as a benchmark and process tool in healthcare organizations. METHODS: A descriptive design was used in a single institution employing a self-report survey. RESULTS: All of the enabling factors were found to be important to nurses. The lowest-rated factors related to manager-staff relationships, congruent with open-ended responses that identified improving management as the highest priority. Unlike previous studies, job insecurity and trust were not issues, and nurses predominantly (92%) reported that quality care was provided. CONCLUSIONS: A simple enabling index and survey tool can be used to proactively assess work environments, so that conditions can be improved before they lead to morale, sickness, and retention problems. The survey results provide important feedback that can prompt discussions about how workplaces can become healthier, more productive, and rewarding places for nurses.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".