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Background <p>This study evaluates changes in critical thinking among dental healthcare workers after anti-involution training (AIT) and identifies key predictors of training effectiveness.</p> Methods <p>A pre-post quasi-experimental design was conducted with 91 participants. Critical thinking was assessed using the CTDI-CV scale. Spearman correlation, multiple linear regression, and LASSO regression were employed to identify predictors, with machine learning models (Random Forest) used for nonlinear exploration and validation.</p> Results <p>AIT significantly improved critical thinking disposition, with a mean total score increase of 11.813 ± 6.752 (<i>P</i> = 0.001). Cognitive maturity showed the greatest improvement (3.978 ± 6.645, <i>P</i> = 0.004). Multiple linear regression revealed that truth-seeking (β=-1.738, <i>P</i> = 0.019) negatively predicted improvement, while cognitive maturity (β = 1.467, <i>P</i> = 0.016) positively predicted improvement. Random Forest validated these findings (AUC = 0.889).</p> Conclusions <p>AIT effectively enhances critical thinking among dental professionals. Baseline truth-seeking and cognitive maturity are key predictors of training outcomes, informing personalized educational strategies.</p>

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The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics: a pre-post quasi-experimental study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Yuxiang Chen,
  • Anna Zhao,
  • Haoran Yang,
  • Xia Yang,
  • Tingting Cheng,
  • Xianqi Rao,
  • Jianzhong Zhou,
  • Lin Li,
  • Ziliang Li

摘要

Background

This study evaluates changes in critical thinking among dental healthcare workers after anti-involution training (AIT) and identifies key predictors of training effectiveness.

Methods

A pre-post quasi-experimental design was conducted with 91 participants. Critical thinking was assessed using the CTDI-CV scale. Spearman correlation, multiple linear regression, and LASSO regression were employed to identify predictors, with machine learning models (Random Forest) used for nonlinear exploration and validation.

Results

AIT significantly improved critical thinking disposition, with a mean total score increase of 11.813 ± 6.752 (P = 0.001). Cognitive maturity showed the greatest improvement (3.978 ± 6.645, P = 0.004). Multiple linear regression revealed that truth-seeking (β=-1.738, P = 0.019) negatively predicted improvement, while cognitive maturity (β = 1.467, P = 0.016) positively predicted improvement. Random Forest validated these findings (AUC = 0.889).

Conclusions

AIT effectively enhances critical thinking among dental professionals. Baseline truth-seeking and cognitive maturity are key predictors of training outcomes, informing personalized educational strategies.