Psychological distress among healthcare workers accessing occupational health services during the COVID-19 pandemic in Zimbabwe
Bibliographic record
Abstract
BACKGROUND: Healthcare workers (HCWs) have experienced anxiety and psychological distress during the COVID-19 pandemic. We established and report findings from an occupational health programme for HCWs in Zimbabwe that offered screening for SARS-CoV-2 with integrated screening for comorbidities including common mental disorder (CMD) and referral for counselling. METHODS: Quantitative outcomes were fearfulness about COVID-19, the Shona Symptom Questionnaire (SSQ-14) score (cutpoint 8/14) and the number and proportion of HCWs offered referral for counselling, accepting referral and counselled. We used chi square tests to identify factors associated with fearfulness, and logistic regression was used to model the association of fearfulness with wave, adjusting for variables identified using a DAG. Qualitative data included 18 in-depth interviews, two workshops conducted with HCWs and written feedback from counsellors, analysed concurrently with data collection using thematic analysis. RESULTS: Between 27 July 2020-31 July 2021, spanning three SARS-CoV-2 waves, the occupational health programme was accessed by 3577 HCWs from 22 facilities. The median age was 37 (IQR 30-43) years, 81.9% were women, 41.7% said they felt fearful about COVID-19 and 12.1% had an SSQ-14 score ≥ 8. A total of 501 HCWs were offered referral for counselling, 78.4% accepted and 68.9% had ≥1 counselling session. Adjusting for setting and role, wave 2 was associated with increased fearfulness over wave 1 (OR = 1.26, 95% CI 1.00-1.60). Qualitative data showed high levels of anxiety, psychosomatic symptoms and burnout related to the pandemic. Mental wellbeing was affected by financial insecurity, unmet physical health needs and inability to provide quality care within a fragile health system. CONCLUSIONS: HCWs in Zimbabwe experience a high burden of mental health symptoms, intensified by the COVID-19 pandemic. Sustainable mental health interventions must be multisectoral addressing mental, physical and financial wellbeing.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".