Work Engagement as a Measure of Wellness in Pediatric Hospital Medicine
Bibliographic record
Abstract
Background: Literature on wellness in pediatrics is limited and there are no published data specific to pediatric hospital medicine (PHM). Existing literature on wellness focuses largely on physician burnout, but wellness also includes positive traits like work engagement. We sought to assess work engagement and burnout in pediatric hospitalists nationwide. Methods: The study utilized a survey including demographic data, the Utrecht Work Engagement Scale (UWES-17), and the Mini-Z burnout measure. The survey recruited participants via the American Academy of Pediatrics Section on Hospital Medicine Listserv. Results: Totally, 432 of 3,085 (14%) respondents completed the survey with mean total UWES score of 4.36 and 36% reported burnout. As expected, higher work engagement scores correlated with decreased rates of burnout (P < 0.0005). Interestingly, work engagement varied by gender and career stage, with lowest scores found in women in early to mid-career (P < 0.05). Conclusions: In this study we evaluated the wellness of pediatric hospitalists, a group that has not been previously studied, using a combination of burnout and work engagement measures which, while validated, had not been previously used to evaluate physician wellness. This study suggests that wellness interventions could be most effective if targeting women in early to mid-career. Further study is needed to determine causes of decreased work engagement and consider appropriate interventions. Int J Clin Pediatr. 2020;9(4):105-109 doi: https://doi.org/10.14740/ijcp399
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".