The impact of COVID-19 on children: insights from the Western Cape experience
Bibliographic record
Abstract
In the initial phases of the COVID-19 epidemic in South Africa, children experienced relative neglect, as they were deemed to be at low risk for contracting and spreading COVID-19 infection. Overwhelmed by adult infections, the health system responded slowly to children’s complex needs brought about by the epidemic. This in-depth case study outlines the impacts of the COVID-19 epidemic on child health in the Western Cape, and the subsequent remediating responses. The case study draws on multiple data sources including routine data, case examples of health system and organisational responses, and experiential evidence from practitioners across the health system. It draws substantially on a series of advocacy briefs that examine the multi-dimensional impacts of COVID-19 on children in the Western Cape. Approximately 12 000 children (persons under 18) contracted COVID-19 in the province (March 2020 to March 2021). Thousands more were affected by the illness and death of relatives, and by the collateral effects of the epidemic including increased hunger, violence, injury and mental health problems, coupled with the disruption of healthcare services, schooling, early childhood development programmes, and social support networks. Essential child health services were de-escalated and child-health resources were re-allocated for adult COVID-19 care, with both immediate and long-term consequences for child health. A proactive response from child health services, aided by the Western Cape’s relative socio-economic advantage, a strong civil society response, and a stable, well-functioning health system, helped to mitigate harm, but not before significant damage was done. The Western Cape experience signals that, even in a well-resourced setting, children’s needs may be overlooked in times of crisis and there is a critical need for ‘voices’ speaking for and alongside children in all decision-making spaces, and for pro-active, well-planned, child-focused responses.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".