Assessing core, e-learning, clinical and technology readiness to integrate telemedicine at public health facilities in Uganda: a health facility – based survey
Notice bibliographique
Résumé
BACKGROUND: In developing countries like Uganda, there are shortages of health workers especially medical specialists. The referral process is frustrating to both patients and health workers (HWs). This is due to delays in accessing laboratory results/tests, costs of travel with resultant delay in consulting specialists. Telemedicine can help reduce these problems. To facilitate successful and sustainable telemedicine implementation the eHealth readiness of different stakeholders should be undertaken. This study was conducted at public health facilities (HFs) in Uganda to assess eHealth readiness across four domains; core, e-learning, clinical and technology, that might hamper adoption and integration of telemedicine. METHODS: A cross-sectional study using mixed methods for data collection was conducted at health center IVs, regional and national referral hospitals. The study was conducted in three parts. Quantitative data on core, e-learning and clinical readiness domains were collected from doctors and other healthcare providers (nurses/midwives, public health officers and allied healthcare workers). Respondents were categorised into 'aware and used telemedicine', 'aware and not used', 'unaware of telemedicine'. Focus Group Discussions were conducted with patients to further assess core readiness. Technology readiness was assessed using a questionnaire with purposively selected respondents; directors, heads of medical sections, and hospital managers/superintendents. Descriptive statistics and correlations were performed using Spearman's rank order test for relationship between technology readiness variables at the HFs. RESULTS: 70% of health professionals surveyed across three levels of HF were aware of telemedicine and 41% had used telemedicine. However, over 40% of HWs at HC-IV and RRH were unaware of telemedicine. All doctors who had used telemedicine were impressed with it. Telemedicine users and non-users who were aware of telemedicine showed core, clinical, and learning readiness. Patients were aware of telemedicine but identified barriers to its use. A weak but positive correlation existed between the different variables in technology readiness. CONCLUSION: Respondents who were aware of and used telemedicine across all HF levels indicated core, learning and clinical readiness for adoption and integration of telemedicine at the public HFs in Uganda, although patients noted potential barriers that might need attention. In terms of technology readiness, gaps still exit at the various HF levels.
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 | 0,039 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».