Use Patterns and Challenges of the Social Media Platform X Among Physiotherapists in Saudi Arabia: Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: Social media platforms have become salient channels for healthcare professionals' continuous education and professional development. Among them, X (formerly Twitter) is used by physiotherapists for engaging in evidence-based discussions and accessing emerging research. In Saudi Arabia, a country with a high social media penetration rate, the platform offers unique opportunities and challenges for physiotherapy-related knowledge acquisition and networking. OBJECTIVE: This study aimed to determine how physiotherapists in Saudi Arabia engage in physiotherapy-related debates on X, explore their usage patterns, and identify associated challenges and perceived professional benefits. METHODS: We conducted a cross-sectional online survey among licensed physiotherapists registered with the Saudi Commission for Health Specialties. The questionnaire covered demographic data, social media usage, interaction patterns, perceived challenges, and motivations for use. Descriptive statistics and chi-square tests were used to examine demographic data, usage patterns, challenges and concerns, perceived professional benefits, as well as the association between demographic characteristics and usage patterns. Statistical significance was set at p < .05. RESULTS: Out of 193 responses, 188 were valid and included in data analysis. Among the respondents, 76.06% reported having an active account on X. Most respondents were female (57.98%) and aged 31-40 years (42.02%). The time spent on the platform varied, with 32.87% spending 4-6 hours a week and 27.27% spending less than an hour per week. Respondents' interaction extent was moderate, with 35.66% reporting occasional interaction. The respondents mainly interacted with knowledge-sharing posts (72.34%), followed by training/workshop-related posts (66.66%). The respondents reported difficulty in finding reliable information (52.45%), time constraints (40.56%), communication barriers (48.25%), and conflicts of interest (51.74%) as challenges concerning engaging in physiotherapy-related debates on X. Despite these concerns, many respondents acknowledged the platform's value, as 60.14% agreed it helped them stay updated with emerging research, 68.53% believed it fostered knowledge sharing, and 67.83% believed it enhanced critical thinking among the community. CONCLUSIONS: Physiotherapists in Saudi Arabia demonstrate active engagement with physiotherapy-related content on X for professional development. While the platform offers valuable opportunities for learning and collaboration, notable barriers, such as information credibility and time limitations, must be addressed. Enhancing digital literacy and establishing clear guidelines for professional social media use may help maximize the platform's potential as a tool for continuous development in physiotherapy practice.
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 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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 | 0,000 |
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 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 ».