Development of a scale for measuring the perception of artificial intelligence among mental health consumers

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Cronbach's alpha: AI acceptance and readiness α = 0.92; AI perceived importance α = 0.92; AI perceived risk α = 0.90; AI perceived challenges α = 0.85. PCA supported a four-factor structure (factor loadings and variance explained not reported in abstract)

• Primary Outcome: Psychometric validation of a 20-item, 4-domain AI Perception (AIP) questionnaire among 431 Jordanian mental health consumers

• Nnt Or Sensitivity: Not applicable (instrument development study). Domain mean scores: AI perceived importance 2.18 (SD 0.83); AI perceived risk 2.58 (SD 0.92); AI perceived challenges 2.78 (SD 0.87); AI acceptance and readiness mean not rendered in abstract due to formula error

• Confidence Interval: Not reported for any psychometric parameter

Clinical Application

A 20-item structured interview instrument is feasible in clinical settings with trained interviewers. However, self-administration feasibility has not been assessed. The structured interview format adds resource burden in routine clinical practice. Digital or tablet-based self-completion formats would require separate usability testing This instrument has no current direct applicability to Australian clinical practice. Australian mental health consumers differ substantially in cultural background, digital literacy, healthcare system familiarity, and attitudes toward technology. The RACGP and Australian Digital Health Agency have published frameworks for digital health consumer engagement, but no validated Australian consumer AI perception instrument currently exists — representing a genuine research opportunity. Any Australian adaptation would require: (1) cultural and linguistic adaptation, (2) cognitive interviewing with Australian consumers including Aboriginal and Torres Strait Islander peoples, (3) confirmatory factor analysis in an Australian sample, and (4) alignment with the Australian Government's AI Ethics Framework and the TGA's evolving regulatory guidance on AI-enabled medical devices. The PBS and MBS do not currently fund AI-specific mental health interventions, making consumer readiness assessment a pre-implementation priority for health services planning The AIP tool, as currently validated, is applicable only to Jordanian mental health clinic attendees. Broader application to other mental health consumer populations requires independent cross-cultural validation, translation, and confirmatory psychometric testing

Abstract

BACKGROUND: Artificial Intelligence (AI) has emerged as a transformative force revolutionizing various sectors, including healthcare, particularly the mental health field. However, the acceptance and integration of AI technologies in different healthcare systems can be influenced by various factors, including cultural, social, and individual aspects. Nevertheless, there is a need for a valid and reliable tool for assessing AI's perception among healthcare consumers. AIM: To develop and validate a tool for the perception of AI among healthcare consumers and apply the tool to assess AI's perception among mental health consumers in the Jordanian healthcare system. METHOD: A cross-sectional descriptive correlational design was utilized in the study. Data was collected from a convenience sample of 431 mental health consumers visiting mental health clinics of the International Medical Corps and university hospitals in Jordan. Structured interviews were conducted using an AI Perception (AIP) questionnaire developed by the authors. The questionnaire's content validity was assessed by an expert panel. Using Principal Component Analysis (PCA), the construct validity of the tool was evaluated, and its internal consistency was examined using Cronbach's alpha. Descriptive statistics were used to assess the levels of AI perception among participants. RESULTS: The final AIP tool consisted of 20 items across 4 domains and has demonstrated strong internal consistency across its four domains: AI acceptance and readiness (α = 0.92), AI perceived importance (α = 0.92), AI perceived risk (α = 0.9), and AI perceived challenges (α = 0.85). The construct validity of the four-domain structure of the tool was supported by PCA. Additionally, the mean scores for each domain indicated the average level of agreement with AI perception items among participants. Specifically, the mean score for AI acceptance and readiness was [Formula: see text]). AI perceived importance was (2.18 [Formula: see text]0.83), AI perceived risk was (2.58[Formula: see text]0.92), and AI perceived challenge was (2.78 [Formula: see text] 0.87). CONCLUSION: The findings of this study resulted in developing a valid and reliable 20-item tool to assess AI's perception among mental health consumers. The tool can be used to assess the predictors of AI's readiness among mental health consumers. Therefore, aiding policymakers and other stakeholders in understanding the AI adoption barriers from the perspective of end-users. In addition, this study developed the AIP tool that can be validated and used among other populations in future research.

References

1. 1.Al-Daraiseh, R., Ta'an, W., Mukattash, T., Al-Hammouri, M. M., Abu-Farha, R., & Williams, B. (2026). Development of a scale for measuring the perception of artificial intelligence among mental health consumers. PLoS ONE. https://doi.org/10.1371/journal.pone.0354305