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Record W4409830485 · doi:10.1177/07342829251335362

Parental Anxiety About Children’s Education: Construct Clarification and Scale Development

2025· article· en· W4409830485 on OpenAlexaff
Guo Xiaolin, Jiajia Xie, He Surina, Ti SU, Liang Luo

Bibliographic record

VenueJournal of Psychoeducational Assessment · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsPsychologyConstruct (python library)AnxietyConstruct validityDevelopmental psychologyScale (ratio)Test validityRating scalePsychometricsClinical psychology

Abstract

fetched live from OpenAlex

Parents’ anxiety about their children’s education has been common in recent years and has a negative effect on children. This study aimed to clarify this construct and develop the Parental Anxiety about Children’s Education Scale (PACES) to lay a foundation for future research. In Study 1, we developed a scale based on 465 parents that included 20 items and four dimensions of anxiety in relation to children’s academic performance, family educational resources, the quality of school education, and the educational macroenvironment. In Study 2, we used data from 4566 parents to test the reliability and validity of the final scale via item analysis, confirmatory factor analysis, consistency reliability, measurement invariance, and validity evidence on the basis of relations. We concluded that the scale is a promising new measure for assessing parental anxiety about children’s education for parents of both primary and secondary school students.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.395
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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