The non-linear association of physical fatigue with depression and anxiety among mental health professionals who recovered from COVID-19 infection: a national survey in China
Bibliographic record
Abstract
Background: The COVID-19 pandemic took a toll on everyone's health and mental health professionals were no exception. This study examined the trajectory of the relationship between levels of physical fatigue and each of depression and anxiety in mental health professionals (MHPs) recovering from COVID-19. Methods: A national survey of 9,858 MHPs who had recovered from COVID-19 was conducted between January and February 2023. The nine-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder (GAD-7) scale, and a numerical rating scale were used to measure depression, anxiety and physical fatigue, respectively. Logistic regression with restricted cubic spline (RCS) models were created to examine the association of physical fatigue with depression and anxiety. Results: The prevalence of depression and anxiety in MHPs who recovered from COVID-19 infection were 47.0% (95%CI: 46.0-48.0%) and 28.9% (95%CI: 28.0-29.8%) respectively. The prevalence of moderate to severe physical fatigue was 44.2% (95%CI: 43.2-45.2%). The RCS models revealed a significant nonlinear relationship between physical fatigue and both depression and anxiety, with an inflection point at a fatigue score of 4. Above this threshold, the risk of both conditions increased significantly. Participants with poor perceived health and lower socioeconomic status had a significantly greater increase in depression and anxiety when fatigue levels were higher. Conclusions: Moderate to severe physical fatigue was associated with depression and anxiety in MHPs recovering from COVID-19. Interventions aimed at alleviating fatigue may play a critical role in improving mental health outcomes in this vulnerable population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".