“I feel broken”: Chronicling burnout, mental health, and the limits of individual resilience in nursing
Bibliographic record
Abstract
Healthcare systems and health professionals are facing a litany of stressors that have been compounded by the pandemic, and consequently, this has further perpetuated suboptimal mental health and burnout in nursing. The purpose of this paper is to report select findings from a larger, national study exploring gendered experiences of mental health, leave of absence (LOA), and return to work from the perspectives of nurses and key stakeholders. Given the breadth of the data, this paper will focus exclusively on the qualitative results from 53 frontline Canadian nurses who were purposively recruited for their workplace insight. This paper focuses on the substantive theme of "Breaking Point," in which nurses articulated a multiplicity of stress points at the individual, organizational, and societal levels that amplified burnout and accelerated mental health LOA from the workplace. These findings exemplify the complexities that underlie nurses' mental health and burnout and highlight the urgent need for multipronged individual, organizational, and structural interventions. Robust and timely interventions are needed to restore the health of the nursing profession and sustain its future.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".