Registered Nurses and The Culture of Nursing Burnout in a Canadian Surgical Burn Unit
Bibliographic record
Abstract
The Canadian health care system is facing critical nursing shortages resulting in extensive wait lists and diminishing quality of care due to, among other things, burnout and turnover of nurses. Burnout among nurses has traditionally been researched from an individualistic lens; in other words, the nurse experiencing burnout is studied. However, by researching burnout from a cultural perspective, I was able to learn about aspects of burnout that extend beyond individual nurses. In order to address nursing burnout, it is important to first obtain a thorough understanding of the role that the culture of organizations can play in allowing for burnout. Because individual problems or experiences happen within cultural contexts, they cannot be divorced from each other. In this thesis, I seek to inform this complex subject using an adapted ethnographic approach. This study took place on a surgical burn unit. Five registered nurse participants were observed and eight participants interviewed about their experiences of the unit, its culture, the demands they face, and their coping strategies. Data from ten observational shifts and eight semi-structured interviews, including interviews with key stakeholders, were analyzed. Participants all reported signs and symptoms associated with burnout which were also observed in daily practice. Interestingly, all participants expressed similar experiences of burnout indicative of a culture of nursing burnout within the unit. Varying reasons for this, both stated and observed, are explored in this thesis.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".