Impact of COVID-19 pandemic on the health-related quality of life of frontline workers: the case of seven low-income Eastern African countries
Bibliographic record
Abstract
PURPOSE: This study aimed to explore the potential impact of the COVID-19 pandemic on the health-related quality of life (HRQoL) of humanitarian and healthcare workers and its related factors in seven Eastern African countries (EAC). METHODS: A sample of frontline workers filled out an online cross-sectional survey questionnaire comprising socio-demographic, degree of symptoms of depression, anxiety, insomnia, and distress, alcohol and tobacco consumption, health-related quality of life (HRQoL) using Short Form 6-Dimension version 2 (SF-6Dv2) and Clinical Outcomes in Routine Evaluation 6-Dimension (CORE-6D), and fear of COVID-19 (FCV-19S) questionnaires. Multivariate regressions were conducted to identify independent factors associated with HRQoL. RESULTS: Of total 721 study participants, mean (standard deviation) scores for SF-6Dv2 and CORE-6D were 0.87 (0.18) and 0.81 (0.14), respectively. Participants with an education level below a university degree, having chronic diseases, been tested positive to COVID-19, with traumatic memories, depression, insomnia, distress, and stress were found to have lower HRQoL likelihood in terms of SF-6Dv2 scores during the COVID-19 pandemic. Similarly, participants with chronic diseases, exposure to COVID-19 patients, depression, insomnia, distress, stress, tested positive with COVID-19, and high level of fear of COVID-19, had lower HRQoL likelihood in terms of CORE-6D scores. Participants who were married had higher HRQoL likelihoods in terms of SF-6Dv2 scores. CONCLUSION: Some personal and mental health characteristics, and COVID-19 related factors, were predictors of lower HRQoL of frontline workers in EAC. These findings should be meaningful while designing sustainable interventions and guidelines aiming to improve the HRQoL of frontline workers during a pandemic situation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".