Impact of COVID-19 on healthcare programs in Zimbabwe: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic disrupted healthcare services. Understanding similar epidemic-related disruptions on a broader scope in our local setting is critical for the effective planning of essential services. The study determined the impact of Coronavirus disease(COVID-19) on healthcare programs in Zimbabwe. METHODS: A mixed-methods study compared healthcare service delivery trends from the Ministry of Health and Child Care before, during and post the pandemic. It employed two methods of data collection: Key informant interviews (KII) and secondary data analysis from the Zimbabwe District Health Information Systems 2 (DHIS2). Purposive sampling obtained key informants for interviews whilst 18 healthcare service indicators were identified from the national database. Statistical analysis consisted of an interrupted time series analysis of those indicators preceded by visualization to appreciate trend change. An inductive approach was used to code and identify basic themes which were then triangulated against DHIS2 findings. RESULTS: The study revealed that COVID-19 had a negative impact on health service delivery; increasing disruptions of critical healthcare services, maternal and child health, reproductive health issues, and other specialist services were prominent. The rise in maternal and child mortality cases and caesarean sections could be directly linked to the decline in service delivery during the pandemic. Mitigation strategies that were introduced during the pandemic included the use of community-based services, outreach services, capacity building, and de-congestion of public services. CONCLUSIONS: The pandemic disrupted healthcare delivery, causing service usage to decline due to lockdowns. Response strategies included community services, capacity building, and stakeholder engagement. Future readiness requires epidemic plans, enhanced resources, a multisectoral approach, workforce training, and public education.
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How this classification was reachedexpand
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".