What Can We Learn From the Past? Pandemic Health Care Workers’ Fears, Concerns, and Needs: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Health care workers (HCWs) have been engaged in fighting dangerous epidemics for hundreds of years, more recently in severe acute respiratory syndrome, H1N1, Middle East respiratory syndrome, and now coronavirus disease 2019. A consistent feature of epidemic disease results is that health care systems and HCWs are placed under immense strain. METHODS: A focused narrative review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to examine the main concerns and anxieties faced by HCWs during recent epidemics and to determine the supports deemed most important to those HCWs to keep them at the frontline. PubMed, Web of Science, and the Cochrane Library were searched in March 2020 using terms "Healthcare" OR "Medical" AND "Staff" OR "Workers" OR "Front line" AND "Concerns" OR "Anxiety" OR "Stress" AND "Pandemic" Or "Epidemic." RESULTS: Twenty-five studies that reported the concerns and expectations of an estimated 13,793 HCWs in 10 countries (Canada, China, Greece, Hong Kong, Japan, Liberia, Netherlands, Saudi Arabia, Singapore and Taiwan) during pandemic situations were identified. Health care workers identified personal and family safety, appreciation, and the provision of personal protective equipment and adequate rest as primary concerns. Informal psychological supports were favored over formal employment-based group interventions. DISCUSSION: Despite being hailed by the media as heroes, HCWs face social stigmatization and experienced high levels of anxiety and fear regarding personal safety and the health of their colleagues and family. Health care workers are more likely to seek peer-to-peer psychological support but also benefit from knowing that formal psychological supports are available to them.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it