Evaluating and Enhancing an Educational Intervention to Reduce Smallholder Farmers’ Exposure to Pesticides in Uganda Through a Digital, Systematic Approach to Behavior Change: Protocol for a Cluster-Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Smallholder farmers receive educational interventions on safe pesticide handling by governmental agencies, industries, or nongovernmental organizations to reduce exposure risks. However, existing educational interventions have limited effects on changing behaviors. Targeting psychosocial determinants of behavior change in educational interventions through theory- and evidence-based approaches may enhance their effectiveness. OBJECTIVE: We aim at describing the intervention development and study design of a 3-arm cluster-randomized controlled trial to assess the effects in improving safe pesticide handling and reducing pesticide exposure of (1) an existing educational intervention and (2) a newly developed SMS text messaging intervention based on the Risks, Attitudes, Norms, Abilities, and Self-regulation (RANAS) behavior change approach. METHODS: We enrolled 539 Ugandan smallholder farmers in 12 clusters (subcounties). The clusters, each with 45 farmers, were randomly allocated to one of the three arms: (1) educational intervention, (2) educational intervention+RANAS-based SMS text messages, or (3) control group. The educational intervention comprised a 2-day workshop that targeted multiple aspects of safe pesticide handling, whereas the SMS text messages targeted the use of personal protective equipment (PPE) and were based on the RANAS approach. For intervention development in this study, this approach includes identifying psychosocial determinants of PPE use at baseline and selecting behavior change techniques to target them in SMS text messages. The primary outcomes of the study are (1) pesticide knowledge, attitude, and practice scores indicating performance throughout the educational intervention; and (2) frequency of PPE use. Secondary outcomes are the RANAS-based behavioral determinants of PPE use, the frequency of glove use, algorithm-based pesticide exposure intensity scores, and signs and symptoms of pesticide poisoning. The outcomes were assessed in structured interviews before the intervention (baseline) and at the 12-month follow-up. The effect of the interventions among the arms will be analyzed using the intervention arms and baseline measures as predictors and the follow-up measures as outcomes in linear multivariable mixed models including the clusters as random effects. The mediating psychosocial determinants of the interventions will be assessed in multiple mediation models. RESULTS: The study was conducted from 2020 to 2021-baseline interviews were conducted in October 2020, and the educational intervention was delivered in November 2020. The RANAS-based SMS text messages were developed based on the baseline data for relevant behavioral determinants of PPE use and sent between February 2021 and September 2021. Follow-up interviews were conducted in October 2021. Overall, 539 farmers were enrolled in the study at baseline; 8.3% (45/539) were lost to follow-up by the end of the study. CONCLUSIONS: This study will contribute to a better understanding of the effectiveness and behavior change mechanisms of educational interventions by using an experimental, cluster-randomized study design to improve pesticide handling among smallholder farmers. TRIAL REGISTRATION: International Standard Randomised Controlled Trial Number (ISRCTN) 18237656; https://doi.org/10.1186/ISRCTN18237656. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55238.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».