Building a Healthy Work Environment: A Nursing Resource Team Perspective
Bibliographic record
Abstract
Leadership and staff from the London Health Sciences Centre (LHSC) Nursing Resource Team (NRT), including members of their Continuous Quality Improvement (CQI) Council, attended the first Southern Ontario Nursing Resource Team Conference (SONRTC), held March 2012 in Toronto. The SONRTC highlighted healthy work environments (HWEs), noting vast differences among the province's various organizations. Conversely, CQI Council members anecdotally acknowledged similar inconsistencies in HWEs across the various inpatient departments at LHSC. In fact, the mobility of the NRT role allows these nurses to make an unbiased observation about the culture, behaviours and practices of specific units as well as cross-reference departments regarding HWEs. Studies have documented that HWEs have a direct impact on the quality of patient care. Furthermore, the literature supports a relationship between HWEs and nurse job satisfaction. Based on this heightened awareness, the NRT CQI Council aimed to investigate HWEs at LHSC. The American Association of Critical Care Nurses (AACN) Standards for Establishing and Sustaining Healthy Work Environments was adapted in developing a survey for measuring HWEs based on the perceptions of NRT staff. Each of the departments was evaluated in terms of the following indicators: skilled communication, true collaboration, effective decision-making, appropriate staffing, meaningful recognition and authentic leadership (AACN 2005). Ultimately, the Building a Healthy Work Environment: A Nursing Resource Team Perspective survey was employed with NRT nurses at LHSC, and data was collected for use by leadership and staff for creating HWE strategies aimed at improving the quality of patient care.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".