Emotional exhaustion and psychosomatic symptoms for doctors working in hospitals resulting Coronavirus pandemic outbreak
Bibliographic record
Abstract
Doctors working in hospitals have been under constant physical and psychological pressure resulting Coronavirus pandemic outbreak. Algeria was among the countries to face the health emergency in a period of great uncertainty about the virus and the ways to treat patients. The present study aims to analyse the levels of emotional exhaustion (EE) and psychosomatic symptoms (PS) of Algerian frontline doctors working in hospitals during the Covid-19 emergency, and their relationship with the evaluation of the institutional responses received. A survey was available online during the peak of health system overload. A total of 103 questionnaires were collected [mean age, 41.8 years; SD: +10,7; high-risk zone: 41.7%]. Correlation analyses were applied to investigate the relationship between the measures of emotional exhaustion and psychosomatic symptoms; ANOVA was applied to compare these measures among groups from different risk zones and with different perceived emotional and safety protection. EE and PS were widely experienced by frontline Doctors working in hospitals. Physical and psychological symptoms were amplified by the perceived lack of institutional support. Ensuring PS and hygiene and safety measures is essential to prevent worsening of health and psychosomatic symptoms in frontline Doctors working in hospitals.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".