Pressures in the Ivory Tower: An Empirical Study of Burnout Scores among Nursing Faculty
Bibliographic record
Abstract
(1) Background: The COVID-19 pandemic has exacerbated incidents of burnout among academics in various fields and disciplines. Although burnout has been the subject of extensive research, few studies have focused on nursing faculty. This study aimed to investigate the differences in burnout scores among nursing faculty members in Canada. (2) Method: Using a descriptive cross-sectional design, data were collected via an online survey in summer 2021 using the Maslach Burnout Inventory general survey and analyzed using the Kruskal-Wallis test. (3) Result: Faculty members (n = 645) with full-time employment status, worked more than 45 h, and taught 3–4 courses reported high burnout (score ≥ 3) compared to those teaching 1–2 courses. Although education levels, tenure status or rank, being on a graduate committee, or the percentage of hours dedicated to research and services were considered important personal and contextual factors, they were not associated with burnout. (4) Conclusions: Findings suggest that burnout manifests differently among faculty and at varying degrees. As such, targeted approaches based on individual and workload characteristics should be employed to address burnout and build resilience among faculty to improve retention and sustain the workforce.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".