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Record W4281569913 · doi:10.1177/02724316221104198

A Latent Growth Analysis of Individual Factors Predicting Test Anxiety During the Transition From Elementary to Secondary School

2022· article· en· W4281569913 on OpenAlexafffund
Catherine Fréchette‐Simard, Isabelle Plante, Stéphane Duchesne, Kathryn Everhart Chaffee

Bibliographic record

VenueThe Journal of Early Adolescence · 2022
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsUniversité LavalUniversité du Québec à Montréal
FundersFonds de Recherche du Québec-Société et Culture
KeywordsPsychologyAnxietyTest anxietyDevelopmental psychologyTest (biology)Transition (genetics)Latent growth modelingSet (abstract data type)Clinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

This study aimed to examine the evolution of test anxiety during the transition to secondary school, a challenging period that includes a set of contextual factors that could potentially increase students’ test anxiety. In addition, to further understand the contribution of different individual factors that might increase the susceptibility to test anxiety during this transition, the study examined the role of motivation, achievement, internalizing behaviors and gender in the development of test anxiety. A total of 478 French-speaking students (231 boys, 247 girls) were followed during their transition to secondary school. Latent growth analysis revealed an overall stable trajectory of test anxiety during the transition to secondary school. However, internalizing behaviors and gender moderated the trajectory over time. Additionally, high initial levels of internalizing behaviors, as well as lower grades in mathematics, were associated with initial levels of test anxiety, as measured at the end of elementary school.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.016
GPT teacher head0.271
Teacher spread0.254 · 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

Citations13
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Early AdolescenceSame topicEducation, Achievement, and GiftednessFrench-language works237,207