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Journals / Balıkesir Sağlık Bilimleri Dergisi / 2026 / Volume: 15 Issue: 3

Determinants of artificial intelligence self-efficacy in physiotherapists: The role of attitudes, AI anxiety and sociodemographic factors

İbrahim Karaca(BALIKESIR UNIVERSITY, FACULTY OF HEALTH SCIENCES, DEPARTMENT OF PHYSICAL MEDICINE AND REHABILITATION, DEPARTMENT OF GERIATRIC PHYSICAL THERAPY)0000-0002-4767-4219
Hatice Gül(AKDENİZ ÜNİVERSİTESİ, SAĞLIK HİZMETLERİ MESLEK YÜKSEKOKULU, TERAPİ VE REHABİLİTASYON BÖLÜMÜ, FİZYOTERAPİ PR.)0000-0003-3991-2704
Suat Erel(PAMUKKALE ÜNİVERSİTESİ, FİZİK TEDAVİ VE REHABİLİTASYON YÜKSEKOKULU, FİZYOTERAPİ VE REHABİLİTASYON BÖLÜMÜ, ORTOPEDİK REHABİLİTASYON ANABİLİM DALI)0000-0001-7076-7651
Pages
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Publication Date
2026-10-08
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Overall, the model explained 52.6% of the variance in AI self-efficacy (R² = .526, adjusted R² = .489).

📌 3 Key Takeaways

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Objective & Hypothesis:Determinants of artificial intelligence self-efficacy in physiotherapists: The role of attitudes, AI anxiety and sociodemographic factors ABSTRACT Purpose: This study examined the role of attitudes toward artificial intelligence (AI), AI-related anxiety, and sociodemographic factors in physiotherapists’ AI self-efficacy.
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Key Findings & Data:Material and Methods: In this cross-sectional online survey, 205 physiotherapists completed the Artificial Intelligence Self-Efficacy Scale, the General Attitudes toward Artificial Intelligence Scale, and the Artificial Intelligence Anxiety Scale.
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Conclusion & Impact:Overall, the model explained 52.6% of the variance in AI self-efficacy (R² = .526, adjusted R² = .489).
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Anxiety · Artificial intelligence · Attitudes

Abstract

Determinants of artificial intelligence self-efficacy in physiotherapists: The role of attitudes, AI anxiety and sociodemographic factors ABSTRACT Purpose: This study examined the role of attitudes toward artificial intelligence (AI), AI-related anxiety, and sociodemographic factors in physiotherapists’ AI self-efficacy. Material and Methods: In this cross-sectional online survey, 205 physiotherapists completed the Artificial Intelligence Self-Efficacy Scale, the General Attitudes toward Artificial Intelligence Scale, and the Artificial Intelligence Anxiety Scale. Sociodemographic variables (gender, age, education level, professional experience, employment sector, academic position, prior AI training) were recorded. Multiple linear regression was used to identify factors associated with self-efficacy. Results: The multiple regression model showed that positive attitudes toward AI were the strongest predictor of AI self-efficacy (β = .637, p .05). Overall, the model explained 52.6% of the variance in AI self-efficacy (R² = .526, adjusted R² = .489). Conclusion: AI self-efficacy in physiotherapists appears to be shaped mainly by attitudinal rather than sociodemographic factors. Education and professional development should therefore focus on strengthening positive and critically reflective attitudes toward AI to support its integration into clinical practice. Keywords: Anxiety, Artificial intelligence, Attitudes, Physiotherapy, Self-efficacy.

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Cite This Article

Karaca, İ., Gül, H., Erel, S. (2026). Determinants of artificial intelligence self-efficacy in physiotherapists: The role of attitudes, AI anxiety and sociodemographic factors. Balıkesir Sağlık Bilimleri Dergisi, Volume: 15 Issue: 3. https://doi.org/10.53424/balikesirsbd.1905384

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