Frontline clinician concerns during the COVID-19 pandemic: A qualitative inquiry
Bibliographic record
Abstract
Objective: The COVID-19 pandemic has strained healthcare systems worldwide, placing a high psychological burden on frontline clinicians. There is an urgent need to better understand their stressors and determine if stressors differ by clinical role. The present study assessed the concerns among frontline clinicians across a large healthcare system during the COVID-19 pandemic to inform the development of tailored supportive services.Methods: From March – June 2020, frontline clinicians across the Mass General Brigham healthcare system were invited to register for an adapted mind-body resiliency group program. Clinicians completed pre- and post-program assessments asking them to report their COVID-19-related concerns. Qualitative data were analyzed in aggregate and by clinical role using content analysis to identify overarching domains.Results: Frontline clinicians’ concerns fall within seven domains: concerns for self, patients, family members, staff, existential concerns, systems-level concerns, and job-level concerns. Concerns for self and existential concerns were most commonly reported across clinical roles. Long-term care clinicians were highly concerned about patients’ wellbeing while rehabilitation therapists were highly concerned about their family members’ health. Across groups, nurse practitioners and physician assistants more often reported job-level concerns. Concerns for staff and systems level concerns were less frequently reported across clinical roles.Conclusions: Frontline clinicians share common pandemic-related concerns, but nuances exist among the concerns most frequently reported across clinical roles. Interventions that offer stress management and resiliency training may be helpful for addressing pandemic-related concerns overall. Future research should determine if tailored support services by clinical role may be warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".