Evidence map›Paper›PMID 42756206›Full record

ArticleFrontiers in psychology2026

Machine learning-based analysis of key factors and mediating mechanisms of career decision-making difficulties among higher vocational college students.

Li Zhang, Huadi Wang

Abstract read
In one paragraph

Article in Frontiers in psychology, 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
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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.

Li ZhangSchool of Education Science, Nanjing Normal University, Nanjing, Jiangsu, China.
Huadi WangInstitute of Vocational Education and Lifelong Education, Jiangsu Academy of Educational Sciences, Nanjing, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research explores the mechanisms through which multidimensional predictors shape career decision-making difficulties among vocational college students and identifies disparities in career decision-making difficulties across student subgroups with disparate developmental profiles. Survey participants were recruited from vocational institutions in Jiangsu, Anhui, and Guangxi provinces, with nine predictive constructs selected as research variables: sense of place, parental career-related behaviors, psychological resilience, core self-evaluations, vocational self-concept, meaning in life, career adaptability, future time perspective, and career exploration. The study first employs k-means cluster analysis to categorize student subgroups, followed by random forest modelling to pinpoint pivotal variables contributing to subgroup segmentation. Empirical findings unfold in three core aspects. First, sampled vocational students fall into three distinct clusters defined by developmental resources: high-resource, moderate-resource, and low-resource cohorts based on the nine measured variables. Second, measurable graded discrepancies in career decision-making difficulties emerge across the three subgroups. Third, future time perspective, core self-evaluations, career exploration and psychological resilience stand as dominant predictors differentiating student clusters; specifically, future time perspective negatively forecasts career decision-making difficulties via a serial mediating path sequentially through core self-evaluations, psychological resilience and career exploration. By unpacking subgroup heterogeneity of vocational students' career decision-making difficulties from an integrated multi-factor framework, this study furnishes empirical evidence and practical implications for vocational colleges to design targeted career counselling interventions and mitigate students' struggles in career decision-making.

Indexed as

career decision-making difficultiesdecision treeK-means clusteringrandom forestvocational college students

Identifiers

PMID42756206
PMCPMC13582346

What OpenQuestion holds

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Registered trials

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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.