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Predicting the Intention to Sign an Advance Directive: A Machine Learning Model Accounting for Cultural and

Mei-Chen Su1, Hsiu-Chun Fang2, Lee-Fen Ni3

  • 1School of Nursing, National Taipei University of Nursing and Health Sciences, Taipei City, Taiwan.

Journal of Nursing Scholarship : an Official Publication of Sigma Theta Tau International Honor Society of Nursing
|June 5, 2026
PubMed
Summary

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This summary is machine-generated.

Machine learning models predict Taiwanese adults' intention to sign advance directives (AD). Key predictors include attitude toward advance care planning (ACP), procedural barriers, and family dynamics, informing culturally sensitive nursing care.

Area of Science:

  • Healthcare Decision-Making
  • Artificial Intelligence in Healthcare
  • Sociology of Health

Background:

  • Understanding factors influencing advance directive (AD) completion is crucial for respecting patient autonomy and preferences.
  • Advance care planning (ACP) involves discussing future medical wishes, distinct from the formal legal act of signing an AD.
  • Culturally sensitive approaches are needed to address diverse values and system-level barriers in AD decision-making.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting Taiwanese adults' intention to sign an AD.
  • To identify psychosocial, demographic, and system-level predictors of AD signing intention within a specific cultural context.
  • To differentiate between the ACP process and formal AD completion, examining cultural and systemic influences.
Keywords:
advance care planningadvance directivecultural and system‐level factorsmachine learningnursing practice

Related Experiment Videos

Main Methods:

  • A cross-sectional quantitative study involving 1412 Taiwanese adults.
  • Utilized validated instruments like the Knowledge of Advance Care Planning Questionnaire and Advance Care Planning Attitude Scale.
  • Employed linear regression, random forest, and extreme gradient boosting models, with SHapley Additive exPlanations for model interpretation.

Main Results:

  • The extreme gradient boosting model demonstrated superior predictive accuracy.
  • Attitude toward ACP was the strongest predictor of AD signing intention.
  • System-level barriers (procedural unfamiliarity, high costs) and demographic factors (older age, more children) significantly influenced intention, indicating a preference for family consensus over formal ADs.

Conclusions:

  • Machine learning effectively models the complex interplay of personal, familial, and institutional factors in AD decision-making.
  • Successful transition from ACP dialogue to AD signing depends on cultural values and structural facilitators/barriers.
  • Nurses can support ACP through family dialogues and by navigating system barriers, enabling culturally responsive care and honoring patient autonomy.