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Enregistrement W4366463113 · doi:10.2196/38774

Effect of the Data Collection Method on Mobile Phone Survey Participation in Bangladesh and Tanzania: Secondary Analyses of a Randomized Crossover Trial

2023· article· en· W4366463113 sur OpenAlexvenueno aff
George Pariyo, Ankita Meghani, Dustin G. Gibson, Joseph Ali, Alain Labrique, Iqbal Ansary Khan, Gulam Muhammed Al Kibria, Honorati Masanja, Adnan A. Hyder, Saifuddin Ahmed

Notice bibliographique

RevueJMIR Formative Research · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSurvey Methodology and Nonresponse
Établissements canadiensnon disponible
Organismes subventionnairesCenters for Disease Control and PreventionAustralian GovernmentDepartment of Foreign Affairs and Trade, Australian GovernmentCenters for Disease Control and Prevention FoundationBloomberg PhilanthropiesWorld Health Organization
Mots-clésTanzaniaMobile phonePopulationPhoneMedicineInteractive voice responseRandom digit dialingEnvironmental healthDemographyLogistic regressionGeographyComputer scienceTelecommunications

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Mobile phone surveys provide a novel opportunity to collect population-based estimates of public health risk factors; however, nonresponse and low participation challenge the goal of collecting unbiased survey estimates. OBJECTIVE: This study compares the performance of computer-assisted telephone interview (CATI) and interactive voice response (IVR) survey modalities for noncommunicable disease risk factors in Bangladesh and Tanzania. METHODS: This study used secondary data from a randomized crossover trial. Between June 2017 and August 2017, study participants were identified using the random digit dialing method. Mobile phone numbers were randomly allocated to either a CATI or IVR survey. The analysis examined survey completion, contact, response, refusal, and cooperation rates of those who received the CATI and IVR surveys. Differences in survey outcomes between modes were assessed using multilevel, multivariable logistic regression models to adjust for confounding covariates. These analyses were adjusted for clustering effects by mobile network providers. RESULTS: For the CATI surveys, 7044 and 4399 phone numbers were contacted in Bangladesh and Tanzania, respectively, and 60,863 and 51,685 phone numbers, respectively, were contacted for the IVR survey. The total numbers of completed interviews in Bangladesh were 949 for CATI and 1026 for IVR and in Tanzania were 447 for CATI and 801 for IVR. Response rates for CATI were 5.4% (377/7044) in Bangladesh and 8.6% (376/4391) in Tanzania; response rates for IVR were 0.8% (498/60,377) in Bangladesh and 1.1% (586/51,483) in Tanzania. The distribution of the survey population was significantly different from the census distribution. In both countries, IVR respondents were younger, were predominantly male, and had higher education levels than CATI respondents. IVR respondents had a lower response rate than CATI respondents in Bangladesh (adjusted odds ratio [AOR]=0.73, 95% CI 0.54-0.99) and Tanzania (AOR=0.32, 95% CI 0.16-0.60). The cooperation rate was also lower with IVR than with CATI in Bangladesh (AOR=0.12, 95% CI 0.07-0.20) and Tanzania (AOR=0.28, 95% CI 0.14-0.56). Both in Bangladesh (AOR=0.33, 95% CI 0.25-0.43) and Tanzania (AOR=0.09, 95% CI 0.06-0.14), there were fewer completed interviews with IVR than with CATI; however, there were more partial interviews with IVR than with CATI in both countries. CONCLUSIONS: There were lower completion, response, and cooperation rates with IVR than with CATI in both countries. This finding suggests that, to increase representativeness in certain settings, a selective approach may be needed to design and deploy mobile phone surveys to increase population representativeness. Overall, CATI surveys may offer a promising approach for surveying potentially under-represented groups like women, rural residents, and participants with lower levels of education in some countries.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,265
score de la tête « metaresearch » (Gemma)0,066
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai randomisé · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,554
Score d'incertitude au seuil0,942

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,2650,066
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,003
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,431
Tête enseignante GPT0,626
Écart entre enseignants0,194 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeEssai randomisé
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2023
Routes d'admission1
Résumé présentoui

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