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Record W2972843958 · doi:10.1111/ldrp.12209

Self–Processes of Acceptance, Compassion, and Regulation of Learning in University Students with Learning Disabilities and/or ADHD

2019· article· en· W2972843958 on OpenAlexaff
David Willoughby, Mary Ann Evans

Bibliographic record

VenueLearning Disabilities Research and Practice · 2019
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPsychologySelf-compassionLearning disabilityDevelopmental psychologyCompassionCognitionPopulationSelf-acceptanceClinical psychologyMindfulnessPsychiatry

Abstract

fetched live from OpenAlex

University students with a learning disability (LD) represent a growing fraction of the student population within North America. Although past research has focused on cognitive aspects of living with an LD and/or attention–deficit/hyperactivity disorder (ADHD), social–emotional factors have received less attention. Such factors may play an important role in self–regulation of learning. This study investigated the relations among self–compassion, self–acceptance of an LD, and self–regulated learning in university students with an LD and/or ADHD. Participants were 78 university students who self–identified as possessing an LD and/or ADHD. Variables were measured using an online questionnaire. These students had lower self–compassion scores than found by researchers in other studies. Correlational analyses revealed significant associations among self–acceptance of an LD, self–compassion, and self–regulated learning.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.410
Teacher spread0.342 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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