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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".