Shaping Positive Work Environments for Nurses: The Contributions of Nurses at Various Organizational Levels
Bibliographic record
Abstract
This paper focuses on nurses' perceptions of their individual contributions to the work environment. Fourteen community hospitals participated in the study. A positive nursing work environment was selected in each agency by its Director of Nursing. Selection was based on subjective and objective criteria. All staff nurses, nurse managers and the director of nursing associated with these units were asked to respond to an open-ended question describing their perceived contributions to the work settings. Ninety-two nurses responded for a response rate of 42%. Overall the three themes of People, Practice and Place surfaced from 15 categories of responses. The same three themes surfaced for all three nurse groups but variation was noted with regards to the categories of contributions the groups most frequently reported within the theme. In this time of continuous change throughout the health care system, nurses need to be able to articulate and affirm their important contributions to the effective shaping of positive health care settings. A focus on contributions could assist with team building, leadership development and have an important impact on quality patient care outcomes.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".