Exploring Prevalence and Implications of Burnout Among Nurse Practitioners in Canada
Bibliographic record
Abstract
Introduction: Nurse Practitioners are experiencing unprecedented levels of burnout which is exacerbated by factors unique to this professional group. Additional factors include undervalued professional worth, lack of autonomy, and organizational and systems pressures. This study was conducted to explore NP Burnout in Canada. Methods: The NP Burnout Survey, which was designed to capture the unique factors impacting NP burnout, was delivered to 229 NPs across Canada. Responses underwent descriptive and binomial logistic regression analysis. Results: More than one-third of NPs are experiencing high levels of burnout. NPs with high burnout are 17 times more likely to leave their position and 66 times more likely to leave the profession. Conclusion: Urgent attention and viable solutions are required to mitigate NP’s exodus from the profession. Addressing the issues impacting NP burnout will ensure that this profession will meet its full potential in Canada’s healthcare system.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".