Evidence map›Paper›PMID 42396113›Full record

ArticleFrontiers in psychology2026

Distinguishing math learning and evaluation anxiety: a structural equation modeling study on pathways from teacher support to academic buoyancy.

Yechao Sun, Yangyang Bai

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
–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.

Yechao SunCollege of Teacher Education, Ningbo University, Ningbo, China.
Yangyang BaiCollege of Teacher Education, Ningbo University, Ningbo, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: While building academic resilience is crucial for adolescents facing intense educational demands, the specific mechanisms buffering subject-specific setbacks remain underexplored. This study examines the associations between math teacher support and high school students' academic buoyancy within the highly demanding context of mathematics learning. Methods: Utilizing a cross-sectional survey with convenience sampling (N=411) from two cities, we first established measurement validity via confirmatory factor analysis (CFA), and subsequently employed structural equation modeling (SEM) to test competing models. Results: The results indicated that process-oriented math learning anxiety negatively predicted buoyancy, whereas outcome-oriented evaluation anxiety did not, identifying content anxiety as a core risk factor. Furthermore, teacher support positively predicted buoyancy directly and indirectly through three pathways: by promoting problem-focused coping, and via two distinct chain pathways associated with reduced learning anxiety through adaptive coping strategies. Importantly, the pathway linking teacher support to academic buoyancy via problem-focused coping was significantly stronger than the anxiety-reduction pathways. Discussion: These findings suggest that teacher support relates to enhanced resilience primarily through its association with adaptive coping strategies and reduced content anxiety. Consequently, educational interventions may benefit from addressing content-based cognitive difficulties and cultivating proactive problem-solving abilities over merely alleviating grade-related concerns.

Indexed as

academic buoyancycoping stylesmath evaluation anxietymath learning anxietyteacher support behavior

Identifiers

PMID42396113
PMCPMC13326596

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.