Bibliographic record
Abstract
Day to day care of patients throughout the continuum of life puts nurses at risk for burnout and secondary traumatic stress (STS). This mixed-methods study explores Medical-Surgical (MS) nursing burnout, STS, and support from professional nursing organization leadership and membership perspectives. Seventy-two nurses were recruited from a nursing organization website and surveyed to provide demographic and Professional Quality of Life Scale Version 5 (ProQOLv5) data. After quantitative data collection, nurse respondents were asked if they would like to participate in a Zoom interview. Qualitative data was derived from in-depth interviews of six medical-surgical nurses and free-text responses from three other participants. The nurses interviewed provided detailed personal definitions of burnout, which led to the identification of themes of “awareness” and “triggers: fueling the fire of burnout,” and also exposed the fluctuating nature of burnout. Participants defined STS as “invasiveness into one’s life” and reflected on the impact that it can have both personally and professionally. Despite the fluctuating nature of burnout and the invasiveness of STS, the nurses interviewed revealed a passion for nursing that served as a driving force for the day-to-day struggle, affirming, “I am a med-surg nurse.” Overcoming burnout and STS lead nurses to “lessons learned: I did not know what I did not know,” which focused on personal and professional growth and development. An overarching theme of “dream vs. reality” captures these stabilizing and destabilizing forces at play in bedside MS nursing.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".