Impact of COVID‑19‑Related Fear and Anxiety on Job Attributes: \nA Systematic Review
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID‑19) pandemic has had different effects on different \noccupations. The present study was designed to systematically review the available evidence to \ninvestigate the pandemic on occupational effects. The academic databases of Scopus, PubMed \nCentral, ProQuest, Science Direct, and ISI Web of Knowledge were searched systematically \nbetween December 2019 and February 2021. COVID‑19‑related fear, concern, worry, anxiety, \nand stress in combination with job‑related MeSH terms were used to search the databases. \nThe methodological quality of included papers was assessed using the Newcastle Ottawa Scale \nchecklist. To synthesize data, a qualitative synthesis of findings was performed due to the \nsmall number of included studies (n = 4) and the heterogeneity of the assessed outcomes. Four \nstudies were included in the final analysis. All four studies were cross‑sectional, collected the \ndata online, and comprised 1654 participants from four different countries. Fear of COVID‑19 \nwas associated with increased future career anxiety, perceived job insecurity, organizational and \nprofessional turnover intentions, and decreased job satisfaction. COVID‑19 Anxiety Syndrome \nwas associated with scores on the Work and Social Adjustment Scale. As so few studies have \nbeen conducted, there are no conclusive findings. More studies using valid and reliable measures \nto assess fear/anxiety related to COVID‑19 and its’ association with job attributes are needed. It \nis also recommended that these associations are examined in variety of different jobs.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".