Developing an information literacy competency evaluation framework for Chinese higher vocational students in smart older adult care: a Delphi-AHP study.

Authors

Fangfang Du, Yajuan Zhou, Chaojin Da, Kun Zhang, Feifan Liu, Feifei Wan, Yujing Dong

Published in

Frontiers in public health. Volume 14. Pages 1906092. Epub Jul 29, 2026.

Abstract

The integration of digital technologies into older adult care requires higher vocational students majoring in Smart Older Adult Care to possess information literacy. However, higher vocational education cultivates technical and skilled talents, while undergraduate education focuses on academic research-oriented talents; thus, their information literacy frameworks cannot be interchanged. Yet prior research has largely focused on general university students or healthcare professionals, lacking targeted evaluation tools for higher vocational students. The gap impedes evidence-based teaching assessment and targeted talent development.
To develop an information literacy evaluation framework for higher vocational students majoring in Smart Older Adult Care and calculate the weights of its indicators.
A Delphi-AHP study.
A multi-method study was conducted in China (April-October 2025), integrating a systematic literature review, expert group discussions, a two-round Delphi survey involving 17 experts, and the Analytic Hierarchy Process (AHP).
Expert positivity coefficients were 94.4% in the first round and 100% in the second, with an authority coefficient of 0.841. Kendall's W were 0.217 and 0.246 (both p < 0.001). The final framework consisted of 4 first-level, 13 s-level, and 44 third-level indicators, with all consistency ratios (CR) < 0.1. The first-level weights were as follows: Information Application and Innovation (0.4718), Information Awareness (0.3073), Information Knowledge (0.1228), and Information Ethics and Security (0.0981).
This study developed a preliminary information literacy framework tailored to higher vocational students, emphasizing practical competence. Further psychometric verification is recommended.

PMID:
42591074
Bibliographic data and abstract were imported from PubMed on 13 Aug 2026.

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