Resilient care in times of covid: The stress buddy
Bibliographic record
Abstract
Introduction The COVID-19 outbreak poses a challenge for health care professionals due to a surge in care demands, overwork, fear of contagion and concerns on the availability of protective equipment, and coping with distress of patients and their families. Although there is emerging evidence on prevalence of stress and its predictors, less is known on the trajectory of stress symptoms and the differences between cohorts of health care professionals. Objectives To sustain and restore health care professionals the Leiden University Medical Center has launched the Digital Stress Buddy, a mobile app, to assess psychological stress, depressive symptoms, anxiety and posttraumatic stress symptoms. Methods Participants fill in a 14-item questionnaire on stress and resilience resources, followed by a COVID-related questionnaire and finally a set of validated questionnaires on depression and anxiety (DASS-21), posttraumatic stress-symptoms (IES-R), burn-out (CBI) and resilience (RES). Results To date, 959 health care workers have completed the stress monitor, of whom 223 (23%) showed relevant stress levels. Within this group, anxiety and posttraumatic symptoms were most prevalent (45%), followed by depressive symptoms (15%). Predictors of stress were being female, coping with distress of patients and their families, teleworking, and overwork. Conclusions By identifying vulnerabilities and resilience for psychological distress, we are able to tailor the support interventions for health care workers within our hospital. This is an ongoing study and future follow-up during the second wave of the pandemic will provide more insight on the trajectories of stress-related symptoms. Conflict of interest No significant relationships.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".