Challenges of design, implementation, acceptability, and potential for, biomedical technologies in the Peruvian Amazon
Notice bibliographique
Résumé
Abstract Background Biomedical technologies have the potential to be advantageous in remote communities. However, information about barriers faced by users of technology in general and in remote Indigenous communities is scarce. The purpose of this study was to characterize the leading challenges faced by researchers who have used biomedical technologies in the Peruvian Amazon. Methods This exploratory, qualitative study with a phenomenological approach depicts the lived experience of participants who were researchers with experience working with biomedical technologies in the Peruvian Amazon in the past five years. Analysis was based on three core themes: design, implementation, and acceptability. Sub-themes included environment, community, and culture. Of the 24 potential participants identified and contacted, 14 agreed to participate, and 13 met inclusion criteria and completed semi-structured interviews. Results were sent to each participant with the opportunity to provide feedback and partake in a 30-minute validation meeting. Five participants consented to a follow-up meeting to validate the results and provide further understanding. Results Participants recognized significant challenges, including technologies designed out-of-context, difficulty transporting the technologies through the Amazon, the impact of the physical environment (e.g., humidity, flooding), and limited existing infrastructure, such as electricity and appropriately trained health personnel. Participants also identified cultural factors, including the need to address past experiences with technology and health interventions, understand and appropriately communicate community benefits, and understand the effect of demographics (e.g., age, education) on the acceptance and uptake of technology. Complementary challenges, such as corruption in authority and waste disposal, and recommendations for technological and health interventions such as co-design were also identified. Conclusions This study proposes that technological and health interventions without efforts to respect local cultures and health priorities, or understand and anticipate contextual challenges, will not meet its goal of improving access to healthcare in remote Amazon communities. Furthermore, the implications of corruption on health services, and improper waste disposal on the environment may lead to more detrimental health inequities.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 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 ».