Using Social Media Platforms to Raise Health Awareness and Increase Health Education in Pakistan: Structural Equation Modeling Analysis and Questionnaire Study
Notice bibliographique
Résumé
Background: Current health care education methods in Pakistan use traditional media (eg, television and radio), community health workers, and printed materials, which often fall short of reach and engagement among most of the population. The health care sector in Pakistan has not yet used social media effectively to raise awareness and provide education about diseases. Research on the impact social media can have on health care education in Pakistan may expand current efforts, engage a wider audience, and reduce the disease burden on health care facilities. Objective: This study aims to evaluate the perceptions of health care professionals and paramedic staff regarding social media use to raise awareness and educate people about diseases as a potential means of reducing the disease burden in Pakistan. Methods: The study used two-stage structural equation modeling (SEM). Data analysis used AMOS 26.0 software, adopting scales from previous literature. Four-item scales for each social media usefulness and health awareness construct and 8-item scales for health care education constructs were adopted on the basis of their higher loading in alignment with psychometric literature. A 7-point Likert scale was used to measure each item. Data collection used convenience sampling, with questionnaires distributed to more than 450 health care professionals and paramedic staff from 2 private hospitals in Lahore, Pakistan. There were 389 useful responses received. However, 340 completed questionnaires were included in the data analysis. Results: The study found that all the squared multiple correlation (SMC) values were greater than 0.30. Furthermore, convergent validity was measured using (1) standardized factor loading (found greater than 0.5), (2) average variance explained (found greater than 0.5), and (3) composite reliability (found greater than 0.7). The confirmatory factor analysis (CFA) of the measurement model indicated the fitness of the constructs (Chi-square minimum [CMIN]=357.62; CMIN/degrees of freedom [DF]=1.80; Goodness of Fit [GFI]=0.90; Adjusted Goodness of Fit Index [AGFI]=0.89; Buntler-Bonett Normed Fit Index [NFI]=0:915; Comparative Fit Index [CFI]=0:93; Root Mean Square Residual [RMR]=0:075; Root Mean Square Error of Approximation [RMSEA]=0:055). Moreover, the structural model fitness was also confirmed (CMIN=488.6; CMIN/DF=1.85; GFI=0.861; AGFI=0.893; NFI=0.987; CFI=0.945; RMR=0:079; RMSEA=0.053). Hence, the results indicated that social media usefulness has a positive and significant effect on health awareness (hypothesis 1: β=.669, P<.001), and health awareness has a positive and significant effect on health care education in Pakistan (hypothesis 2: β=.557, P<.001). Conclusions: This study concludes that health care professionals and paramedic staff in private hospitals support the use of social media to raise awareness and provide health care education. It is considered an effective tool for reducing the disease burden in Pakistan. The study results also revealed that young health care professionals are more inclined toward social media usage and express the need for legislation to support it and establish a monitoring process to avoid misinformation.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 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) | 0,000 | 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 ».