ArticleInternational journal of public health2026
AI competency misalignment in preventive medicine: a multi-stakeholder survey with latent profile analysis in Sichuan and Chongqing.
Article in International journal of public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Evaluate supply-demand misalignment in public health artificial intelligence (AI) workforce education in Sichuan and Chongqing, inland Western China. Methods: Surveyed 1,031 stakeholders (150 employers, 680 students, 201 educators) using Importance-Performance Analysis (IPA) and Latent Profile Analysis (LPA) to quantify skill deficits. Multivariate models assessed collaborative training and faculty transfer factors, guided by a conceptual framework integrating demand, supply, and training perspectives. All coefficients are associational, not causal. Results: IPA showed employers prioritised risk assessment; students focused on algorithmic construction. LPA on six practical skills identified two profiles: High-Order Application Group (23.2%) and Foundation-Weak Group (76.8%); the weighted combination of profile means reconciled with the overall sample mean. Employers' deficit perception was positively associated with their collaboration willingness (β = 0.235, 95% CI [0.061, 0.410], p < 0.01). Institutional innovation negatively moderated the link between faculty AI proficiency and research mentorship (β = -0.120, [-0.216, -0.024], p < 0.05). Conclusion: AI education may overemphasize computational skills relative to frontline operational demands. Mitigation may require stratified pedagogy, real-world data, and less administration. Multi-stakeholder framework is valuable; causality requires further research.
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