Looking ahead in the COVID-19 pandemic: emerging lessons learned for sexual and reproductive health services in low- and middle-income countries
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,007 | 0,012 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».