Work Environment, Clinician Attitudes, and Outcomes Relationship Evaluation: The WE CARE Study
Bibliographic record
Abstract
Background: Paediatric nurses care for some of the most vulnerable patients in our healthcare system and the nurses’ well-being in the workplace is directly related to the quality of care provided. However, much of the organizational behaviour research conducted on nursing populations neglects to separately study nurses who care for children. In a resource limited system where health care provider well-being is recognized as a priority, it is important for administrators to know the environmental and attitudinal work factors that are the most influential to paediatric nurse work outcomes in order to target their optimization strategies.Purpose: The aim of this dissertation was to identify which modifiable work environment factors make the greatest contribution to the work outcome of burnout in paediatric nurses and provide nurses and organizational leaders with a targeted focus to optimize work outcomes and provider well-being in a resource limited system based on key stakeholder feedback. Method: This was achieved by conducting a scoping review and a two-phased mixed methods study set in a large quaternary care paediatric hospital in Toronto, Canada. The explanatory sequential mixed methods design consisted of a quantitative cross-sectional survey design followed by qualitative semi-structured interviews. Results: The results of this dissertation are represented through three published manuscripts; a scoping review; a cross-sectional survey design study; and a qualitative semi-structured interview study. We identified and measured the modifiable work environment features and work attitudes associated with burnout in paediatric critical care nurses; ranked the factors to determine their contribution to explaining burnout among paediatric critical care nurses; expanded our understanding of the paediatric critical care nurses’ experience of burnout in relation to their work environment and work engagement ; and provided recommendations to nurses, nursing leadership, and organization administration on targeted, actionable improvements for nursing work outcomes based on key stakeholder feedback. Conclusion: A better understanding of the relationships between paediatric nurses’ work environment, work attitudes, and work outcomes will allow administrators to provide targeted interventions to improve nurse well-being and, ultimately, paediatric 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 | 0.036 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".