Nurses Navigating Mental Health During Uncharted Times: Self, Others, Systems (S.O.S)!
Bibliographic record
Abstract
Study Background The nursing profession is facing a multiplicity of stressors that have both predated and been exacerbated by the Covid-19 pandemic. The emotional and physical demands entailed in nursing predispose nurses to suboptimal mental health and burnout. Purpose This paper draws upon the narrative interviews of 53 Canadian nurses as part of a larger pan-Canadian, cross disciplinary study that examined the gendered experiences of mental health, leaves of absence, and return to work of 7 professions. Methods Thorne's interpretive descriptive guided Iterative and thematic analysis which identified three predominant themes within the nursing dataset, this paper focuses on the substantive theme of ‘ Navigating it Alone,’ Results Nurses expressed a profound sense of isolation at 3 particular levels: at home, at work, and in systems – while simultaneously balancing uniquely gendered familial responsibilities and workplace demands. Conclusions These results illuminate instrumental pathways for stakeholders to attenuate the personal and professional pressures that continue to be disproportionately carried by nurses as they navigate these particularly challenging times.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".