Mobile Technology Use in Clinical Research Examining Challenges and Implications for Health Promotion in South Africa: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: The use of mobile technologies in fostering health promotion and healthy behaviors is becoming an increasingly common phenomenon in global health programs. Although mobile technologies have been effective in health promotion initiatives and follow-up research in higher-income countries and concerns have been raised within clinical practice and research in low- and middle-income settings, there is a lack of literature that has qualitatively explored the challenges that participants experience in terms of being contactable through mobile technologies. OBJECTIVE: This study aims to explore the challenges that participants experience in terms of being contactable through mobile technologies in a trial conducted in Soweto, South Africa. METHODS: A convergent parallel mixed methods research design was used. In the quantitative phase, 363 young women in the age cohorts 18 to 28 years were contacted telephonically between August 2019 and January 2022 to have a session delivered to them or to be booked for a session. Call attempts initiated by the study team were restricted to only 1 call attempt, and participants who were reached at the first call attempt were classified as contactable (189/363, 52.1%), whereas those whom the study team failed to contact were classified as hard to reach (174/363, 47.9%). Two outcomes of interest in the quantitative phase were "contactability of the participants" and "participants' mobile number changes," and these outcomes were analyzed at a univariate and bivariate level using descriptive statistics and a 2-way contingency table. In the qualitative phase, a subsample of young women (20 who were part of the trial for ≥12 months) participated in in-depth interviews and were recruited using a convenience sampling method. A reflexive thematic analysis approach was used to analyze the data using MAXQDA software (version 20; VERBI GmbH). RESULTS: Of the 363 trial participants, 174 (47.9%) were hard to reach telephonically, whereas approximately 189 (52.1%) were easy to reach telephonically. Most participants (133/243, 54.7%) who were contactable did not change their mobile number. The highest percentage of mobile number changes was observed among participants who were hard to reach, with three-quarters of the participants (12/16, 75%) being reported to have changed their mobile number ≥2 times. Eight themes were generated following the analysis of the transcripts, which provided an in-depth account of the reasons why some participants were hard to reach. These included mobile technical issues, coverage issues, lack of ownership of personal cell phones, and unregistered number. CONCLUSIONS: Remote data collection remains an important tool in public health research. It could, thus, serve as a hugely beneficial mechanism in connecting with participants while actively leveraging the established relationships with participants or community-based organizations to deliver health promotion and practice.
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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 | 0,051 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 ».