Mobile App–Reported Use of Traditional Medicine for Maintenance of Health in India During the COVID-19 Pandemic: Cross-sectional Questionnaire Study
Notice bibliographique
Résumé
BACKGROUND: India follows a pluralistic system for strategic and focused health care delivery in which traditional systems of medicine such as Ayurveda, yoga and naturopathy, Unani, Siddha, Sowa Rigpa, and homoeopathy (AYUSH) coexist with contemporary medicine, and this system functions under the Ministry of AYUSH (MoA). The MoA developed a mobile app, called AYUSH Sanjivani, to document the trends of the use of AYUSH-based traditional and holistic measures by the public across India. Analysis of the data generated through this app can help monitor the extent of the use of AYUSH measures for maintenance of health during the COVID-19 pandemic and aid effective health promotion and communication efforts focused on targeted health care delivery during the pandemic. OBJECTIVE: The purpose of the study was to determine the extent of use of AYUSH measures by the public in India for maintenance of health during the COVID-19 pandemic as reported through the AYUSH Sanjivani mobile app. METHODS: Cross-sectional analysis of the data generated through the Ayush Sanjivani app from May 4 to July 31, 2020, was performed to study the pattern and extent of the use of AYUSH-based measures by the Indian population. The responses of the respondents in terms of demographic profile, use pattern, and benefits obtained; the association between the use of AYUSH-based measures and symptomatic status; and the association between the duration of use of AYUSH-based measures and the outcome of COVID-19 testing were evaluated based on bivariate and multivariate logistic regression analysis. RESULTS: Data from 723,459 respondents were used for the analysis, among whom 616,295 (85.2%) reported that they had been using AYUSH measures for maintenance of health during the COVID-19 pandemic. Among these 616,295 users, 553,801 (89.8%) either strongly or moderately agreed to have benefitted from AYUSH measures. Ayurveda and homeopathic measures and interventions were the most preferred by the respondents across India. Among the 359,785 AYUSH users who described their overall improvement in general health, 144,927 (40.3%) rated it as good, 30,848 (8.6%) as moderate, and 133,046 (40.3%) as slight. Respondents who had been using AYUSH measures for less than 30 days were more likely to be COVID-19-positive among those who were tested (odds ratio 1.52, 95% CI 1.44-1.60). The odds of nonusers of AYUSH measures being symptomatic if they tested positive were greater than those of AYUSH users (odds ratio 4.01, 95% CI 3.61-4.59). CONCLUSIONS: The findings of this cross-sectional analysis assert that a large proportion of the representative population practiced AYUSH measures across different geographic locations of the country during the COVID-19 pandemic and benefitted considerably in terms of general well-being, with a possible impact on their quality of life and specific domains of health.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».