Effect of the COVID-19 Pandemic Preparation and Response on Essential Health Services in Primary and Tertiary Healthcare Settings of Amhara Region, Ethiopia
Bibliographic record
Abstract
Countries like Ethiopia have had to make difficult decisions to balance between the demands of the COVID-19 pandemic and maintaining the essential health service delivery. We assessed the effect of preventive COVID-19 measures on essential healthcare services in selected health facilities of Ethiopia. In a comparative cross-sectional study, we analyzed and compared data from seven health facilities over two periods: the pre-COVID-19 period before the first reported COVID-19 case in the country and during the COVID-19 period. Data were summarized using descriptive statistics and the independent t test. During the COVID-19 period the average number of monthly patient visits in the emergency department, pediatrics outpatient, and adult outpatient dropped by 27%, 30%, and 27%, respectively compared with the pre-COVID-19 period. Family planning; institutional delivery; childhood immunization; antenatal care-, hypertension- and diabetic patient follow-up, did not vary significantly between pre-COVID-19 and during COVID-19. Moreover, the monthly average number of tuberculosis (TB) and HIV patients who visited health facilities for drug refill and clinical evaluation did not vary significantly during the two periods. In conclusion, the study highlights that the effect of public restrictions to mitigate the COVID-19 pandemic on essential care systems should be considered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".