Staff Mental Health Self-Assessment During the COVID-19 Outbreak
Bibliographic record
Abstract
In a survey in Canada about the psychosocial effects of Severe Acute Respiratory Syndrome on hospital staff, 29% of the respondents scored above the threshold point on the 12-item General Health Questionnaire, indicating probable emotional distress.1 Frontline healthcare workers may experience fear of being infected and spreading the virus to their families, particularly those working in isolation wards and accident and emergency departments. There is a need for timely mental health care for patients and health workers during the COVID-19 outbreak.2 With the high anticipated stress among hospital staff in Hong Kong East Cluster, a comprehensive support programme named Support of You (SOY) is initiated. Since 14 February 2020, the Department of Psychiatry of Pamela Youde Nethersole Eastern Hospital has provided an online mental health self-assessment questionnaire to all hospital staff in the cluster. In China, a national guideline has been established for emergency psychological crisis intervention for the COVID-19 outbreak, and it recommends that (1) frontline staff involving in the care of COVID-19[-]infected patients should receive prior psychological crisis intervention trainings to anticipate patient's psychological reactions;(2) staff working in isolation ward should be put on rotations;(3) accommodation should be provided to frontline staff for self-isolation;(4) hotline and online psychological crisis interventions should be provided to needed staff;(5) and a psychological response team consisting of psychiatrists, psychologists, and psychiatric nurses should be formed in each unit to provide staff with psychological support.3 Enhancing the psychological well-being of hospital staff during the COVID-19 outbreak is equally important to the fight against the outbreak.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".