Additional file 1 of Evolution of burnout and psychological distress in healthcare workers during the COVID-19 pandemic: a 1-year observational study
Bibliographic record
Abstract
Additional file 1: Table S1. Socio-demographic, occupational data, COVID-19 specific characteristics of participants who responded at both times-point surveys (3 & 12-month surveys responders, n = 394).Table S2. Adjusted coefficient, 95% confidence interval and p-values from multivariable logistic regression model including self-compassion variable for burnout status among healthcare workers 12 months after the onset of COVID-19 pandemic (12-month survey responders, n = 336; 74 missings). Table S3. Adjusted coefficient, 95% confidence interval and p-values from multivariable linear regression model including self-compassion variable for posttraumatic stress symptoms among healthcare workers 12 months after the onset of COVID-19 pandemic (12-month survey responders, n = 343; 67 missings).Table S4. Adjusted coefficient, 95% confidence interval and p-values from multivariable linear regression model including self-compassion variable for anxiety symptoms among healthcare workers 12 months after the onset of COVID-19 pandemic (12-month survey responders, n = 341; 69 missings).Table S5. Adjusted coefficient, 95% confidence interval and p-values from multivariable linear regression model including self-compassion variable for depression symptoms among healthcare workers 12 months after the onset of COVID-19 pandemic (12-month survey responders, n = 341; 69 missings).
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.510 | 0.024 |
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".