Pediatric hematology/oncology healthcare professional emotional health during COVID‐19
Bibliographic record
Abstract
OBJECTIVES: Little is known about the impact of coronavirus disease 2019 (COVID-19) on healthcare professional emotional health in pediatric hematology/oncology. Primary objective was to describe anxiety, depression, positive affect, and perceived stress among pediatric hematology/oncology healthcare professionals following a COVID-19 outbreak. Secondary objectives were to compare these outcomes based on contact with a positive person, and to identify risk factors for worse outcomes. MATERIALS AND METHODS: We included 272 healthcare professionals working with pediatric hematology/oncology patients. We determined whether respondents had direct or indirect contact with a COVID-19-positive individual and then measured outcomes using the Patient-Reported Outcomes Measurement Information System (PROMIS) depression, anxiety, and positive affect measures, and the Perceived Stress Scale. RESULTS: Among eligible respondents, 205 agreed to participate (response rate 75%). Sixty-nine (33.7%) had contact with a COVID-19-positive person. PROMIS anxiety, depression, and positive affect scores were similar to the general United States population. Those who had contact with a COVID-19-positive individual did not have significantly different outcomes. In multiple regression, non-physicians had significantly increased anxiety (nurses: p = 0.013), depression (nurses: p = 0.002, pharmacists: p = 0.038, and other profession: p = 0.021), and perceived stress (nurses: p = 0.002 and other profession: p = 0.011) when compared to physicians. CONCLUSIONS: Pediatric hematology/oncology healthcare professionals had similar levels of anxiety, depression, and positive affect as the general population. Contact with a COVID-19-positive individual was not significantly associated with outcomes. Non-physician healthcare professionals had more anxiety, depression, and perceived stress when compared to physicians. These findings may help to develop programs to support healthcare professional resilience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".