Finding the Best Way to Deliver Online Educational Content in Low-Resource Settings: Qualitative Survey Study
Notice bibliographique
Résumé
BACKGROUND: The reach of internet and mobile phone coverage has grown rapidly in low- and middle-income countries (LMICs). The potential for sharing knowledge with health care workers in low-resource settings to improve working practice is real, but barriers exist that limit access to online information. Burns affect more than 11 million people each year, but health care workers in low-resource settings receive little or no training in treating burn patients. Interburns' training programs are tailor-made to improve the quality of burn care in Asia, Africa, and the Middle East; the challenge is to understand the best way of delivering these resources digitally toward improved treatment and care of burn patients. OBJECTIVE: The aim of the study, funded by the National Institute for Health Research (NIHR), was to understand issues and barriers that affect health care worker access to online learning in low-resource settings in order to broaden access to Interburns' training materials and improve burn-patient care. METHODS: A total of 546 participants of Interburns' Essential Burn Care (EBC) course held in Bangladesh, Nepal, Ethiopia, and the West Bank, the occupied Palestinian Territories, between January 2016 and June 2018 were sent an online survey. EBC participants represent the wide range of health care professionals involved with the burn-injured patient. A literature review was carried out as well as research into online platforms. RESULTS: A total of 207 of 546 (37.9%) participants of the EBC course did not provide an email address. Of the 339 email addresses provided, 81 (23.9%) "bounced" back. Surgeons and doctors were more likely to provide an email address than nurses, intern doctors, or auxiliary health care workers. A total of 258 participants received the survey and 70 responded, giving a response rate of 27.1%. Poor internet connection, lack of time, and limited access to computers were the main reasons for not engaging with online learning, along with lack of relevant materials. Computers were seen as more useful for holding information, while mobile phones were better for communicating and sharing knowledge. Health care workers in LMICs use mobile phones professionally on a daily basis. A total of 80% (56/70) felt that educational content on burns should be available through mobile apps. CONCLUSIONS: Health care workers in low-resource settings face a variety of barriers to accessing educational content online. The reliance on email for sign-up to learning management systems is a significant barrier. Materials need to be relevant, localized, and easy to consume offline if necessary, to avoid costs of mobile phone data. Smartphones are increasingly used professionally every day for communication and searching for information, pointing toward the need for tailored educational content to be more available through mobile- and web-based apps.
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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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».