The Utility of SMS to Report Male Partner HIV Self-testing Outcomes Among Women Seeking Reproductive Health Services in Kenya: Cohort Study
Notice bibliographique
Résumé
BACKGROUND: Use of SMS for data collection is expanding, but coverage, bias, and logistical constraints are poorly described. OBJECTIVE: The aim of this study is to assess the use of SMS to capture clinical outcomes that occur at home and identify potential biases in reporting compared to in-person ascertainment. METHODS: In the PrEP Implementation in Young Women and Adolescents program, which integrated pre-exposure prophylaxis (PrEP) into antenatal care, postnatal care, and family planning facilities in Kisumu County, Kenya, HIV-negative women 14 years of age or older were offered oral HIV self-tests (HIVSTs) to take home to male partners. Women that brought a phone with a Safaricom SIM to the clinic were offered registration in an automated SMS system (mSurvey) to collect information on HIVST outcomes. Women were asked if they offered the test to their male partners, and asked about the test process and results. HIVST outcomes were collected via SMS (sent 2.5 weeks later), in-person (if women returned for a follow-up scheduled 1 month later), or using both methods (if women initiated PrEP, they also had scheduled follow-up visits). The SMS prompted women to reply at no charge. HIVST outcomes were compared between women with scheduled follow-up visits and those without (follow-up visits were only scheduled for women who initiated PrEP). HIVST outcomes were also compared between women reporting via SMS and in-person. RESULTS: Among 2123 women offered HIVSTs and mSurvey registration, 486 (23.89%) accepted HIVSTs, of whom 359 (73.87%) were eligible for mSurvey. Additionally, 76/170 (44.7%) women with scheduled follow-up visits and 146/189 (77.3%) without scheduled follow-up visits registered in mSurvey. Among the 76 women with scheduled follow-ups, 62 (82%) had HIVST outcomes collected: 19 (31%) in-person, 20 (32%) by SMS, and 23 (37%) using both methods. Among the 146 women without scheduled visits, 87 (59.6%) had HIVST outcomes collected: 3 (3%) in-person, 82 (94%) by SMS, and 2 (2%) using both methods. SMS increased the collection of HIVST outcomes substantially for women with scheduled follow-up visits (1.48-fold), and captured 82 additional reports from women without scheduled follow-up visits. Among 222 women with reported HIVST outcomes, frequencies of offering partners the HIVST (85/95, 89% in-person vs 96/102, 94% SMS; P=.31), partners using the HIVST (83/85, 98% vs 92/96, 96%; P=.50), women using HIVST with partners (82/83, 99% vs 91/92, 99%; P=.94), and seeing partner's HIVST results (82/83, 99% vs 89/92, 97%; P=.56) were similar between women reporting in-person only versus by SMS only. However, frequency of reports of experiencing harm or negative reactions from partners was more commonly reported in the SMS group (17/102, 16.7% vs 2/85, 2%; P=.003). Barriers to the SMS system registration included not having a Safaricom SIM or a functioning phone. CONCLUSIONS: Our results suggest that the use of SMS substantially improves completeness of outcome data, does not bias reporting of nonsensitive information, and may increase reporting of sensitive information. .
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 | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».