Looking ahead in the COVID-19 pandemic: emerging lessons learned for sexual and reproductive health services in low- and middle-income countries
Bibliographic record
Abstract
The COVID-19 pandemic has caused widespread disruption to essential health service provision globally, including in low- and middle-income countries (LMICs). Recognising the criticality of sexual and reproductive health (SRH) services, we review the actual reported impact of the COVID-19 pandemic on SRH service provision and evidence of adaptations that have been implemented to date. Across LMICs, the available data suggests that there was a reduction in access to SRH services, including family planning (FP) counselling and contraception access, and safe abortion during the early phase of the pandemic, especially when movement restrictions were in place. However, services were quickly restored, or alternatives to service provision (adaptations) were explored in many LMICs. Cases of gender-based violence (GBV) increased, with one in two women reporting that they have or know a woman who has experienced violence since the beginning of the pandemic. As per available evidence, many adaptations that have been implemented to date have been digitised, focused on getting SRH services closer to women. Through the pandemic, several LMIC governments have provided guidelines to support SRH service delivery. In addition, non-governmental organisations working in SRH programming have played significant roles in ensuring SRH services have been sustained by implementing several interventions at different levels of scale and to varying success. Most adaptations have focused on FP, with limited attention placed on GBV. Many adaptations have been implemented based on guidance and best practices and, in many cases, leveraged evidence-based interventions. However, some adaptations appear to have simply been the sensible thing to do. Where evaluations have been carried out, many have highlighted increased outputs and efficiency following the implementation of various adaptations. However, there is limited published evidence on their effectiveness, cost, value for money, acceptability, feasibility, and sustainability. In addition, the pandemic has been viewed as a homogenous event without recognising its troughs and waves or disentangling effects of response measures such as lockdowns from the pandemic itself. As the pandemic continues, neglected SRH services like those targeting GBV need to be urgently scaled up, and those being implemented with any adaptations should be rigorously tested.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".