AUTHOR=Pan Qiuyu , Liu Nian 
  
TITLE=AI competency misalignment in preventive medicine: a multi-stakeholder survey with latent profile analysis in Sichuan and Chongqing
  
JOURNAL=International Journal of Public Health
  
VOLUME=Volume 71 - 2026
  
YEAR=2026
  
URL=https://www.ssph-journal.org/journals/international-journal-of-public-health/articles/10.3389/ijph.2026.1610073
  
DOI=10.3389/ijph.2026.1610073
  
ISSN=1661-8564
  
ABSTRACT=ObjectiveEvaluate supply-demand misalignment in public health artificial intelligence (AI) workforce education in Sichuan and Chongqing, inland Western China.MethodsSurveyed 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.ResultsIPA 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).ConclusionAI 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.