Psychosocial Challenges of the Coronavirus Disease-2019 Pandemic Among Frontline Health Care Providers and Their Coping Mechanisms at Mbarara Regional Referral Hospital, Southwestern Uganda
Bibliographic record
Abstract
Background: As a novel global health pandemic, Coronavirus Disease-2019 (COVID-2019) has posed various challenges to frontline healthcare providers (FHCPs). This study explored the social and psychological challenges of COVID-19 to the FHCPs at Mbarara Regional Referral Hospital, southwestern Uganda. Methods: This was a cross-sectional study with a qualitative approach. Participants were purposively selected, consented, and interviewed. Interviews were audio-recorded and transcribed. Data were entered into NVivo 10 software and analyzed using a thematic analysis approach. Results: Fourteen FHCPs with diverse roles, including 8 men, were interviewed. Participants' median age was 38 years (range: 26-51 years) and eleven of them were married. The subjects' experiences were explored in relation to perceived social and psychological challenges of working during the COVID-19 pandemic, and coping mechanisms in the COVID-19 pandemic situation. The social challenges identified were burnout, domestic violence, and a financially constrained environment. A further, psychological challenge was anxiety, as well as fear and distress. FHCPs responded with a variety of coping mechanisms, including situational acceptance, religious coping, coping through emotional support of others, and bulk purchase of supply-constrained basic necessities. Conclusion: FHCPs experienced numerous social and psychological challenges, which negatively affected their quality of life amidst a wavering pandemic. As the pandemic rages on, creative and low-cost psychosocial interventions for FHCPs are needed, possibly including more formal peer support, and an improved flow of information about ongoing infectious disease control interventions, so FHCPs feel more knowledgeable about what is ahead.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".