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Record W3179598871 · doi:10.1080/02699931.2021.1955243

The subject matters: relations among types of anxiety, ADHD symptoms, math performance, and literacy performance

2021· article· en· W3179598871 on OpenAlexafffund
Sabrina Di Lonardo Burr, Jo‐Anne LeFevre

Bibliographic record

VenueCognition & Emotion · 2021
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologySubject (documents)AnxietyLiteracyDevelopmental psychologyCognitive psychologyClinical psychologyPsychiatryPedagogy

Abstract

fetched live from OpenAlex

= 515) completed self-report measures of anxiety (i.e. trait, test, math, and literacy) and ADHD symptoms. During the study, they completed math and literacy measures and reported their current (i.e. state) anxiety three times. There were significant correlations among ADHD symptoms and all measures of anxiety. However, neither ADHD symptoms nor trait anxiety were correlated with math or literacy performance. Test-anxious students reported increasing state anxiety as the study progressed. In contrast, math-anxious students reported increased state anxiety following the math tasks but not the literacy tasks and literacy-anxious students reported increased state anxiety following the literacy tasks but not the math tasks. With respect to performance, math-anxious students had worse math performance and literacy-anxious students had worse literacy performance whereas test anxiety was not directly related to performance on either math or literacy tasks. Patterns of relations did not differ for men and women, but, except for literacy anxiety, women reported higher levels of all types of anxiety. These results could help educators understand how affective responses are related to university students' academic performance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.178
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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 teacher head, 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

Citations11
Published2021
Admission routes2
Has abstractyes

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