Evidence map›Paper›PMID 42694394›Full record

ArticleFrontiers in public health2026

Exploring care adoption intention among older adults using XGBoost: a rural case in Anhui, China.

Jingli Liu, Ziwei Yang

Abstract read
In one paragraph

Article in Frontiers in 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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jingli LiuSchool of Humanities and Social Sciences, Anhui University of Science and Technology, Huainan, China.
Ziwei YangSchool of Management, Hefei University of Technology, Hefei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: With the development of digital rural construction and rural mutual-aid care for older adults, smart mutual-aid care for older adults has become an important way to improve rural pension services, yet low digital literacy and insufficient adoption intention among rural older adults hinder the popularization of smart care services for older adults. Methods: Based on the Technology Acceptance Model, this study collected 320 valid questionnaires from rural older adults in Anhui Province and combined traditional statistical approaches with explainable machine learning to explore factors affecting service adoption intention. Results: The questionnaire possessed good reliability and validity with significant correlations across all core variables; perceived ease of use and perceived usefulness significantly boosted adoption intention, while social support lost statistical significance due to collinearity suppression. Perceived ease of use served as the dominant driving factor, social support contributed positively independently, the explanatory power of perceived usefulness decreased, and all variables generated positive nonlinear marginal effects on adoption intention. Discussion: These findings offer empirical evidence and practical references for optimizing and promoting rural smart mutual-aid care services for older adults in a targeted manner.

Indexed as

IntentionPatient Acceptance of Health CareRural PopulationAgedAged, 80 and overBoosting Machine Learning AlgorithmsChinaDigital HealthFemaleHumansMaleReproducibility of ResultsSocial SupportSurveys and Questionnairesadoption intentionrural older adultssmart mutual-aid caretechnology acceptance modelXGBoost

Identifiers

PMID42694394
PMCPMC13538432

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.