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Record W2155683732 · doi:10.1177/0022219411422260

Internalizing Problems of Adults With Learning Disabilities

2011· review· en· W2155683732 on OpenAlexaff
Robert M. Klassen, Virginia M. C. Tze, Wanwisa Hannok

Bibliographic record

VenueJournal of Learning Disabilities · 2011
Typereview
Languageen
FieldPsychology
TopicPsychology of Development and Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyLearning disabilityDevelopmental psychologyCognitive psychology

Abstract

fetched live from OpenAlex

In this article the authors report a meta-analysis that examines the association between internalizing problems (anxiety and depressive symptoms) and learning disabilities (LD) in adults. Two hypotheses about the relationship between internalizing problems and LD in adults are posited and tested: the abeyance hypothesis (internalizing problems decline in adulthood) and the continuance hypothesis (internalizing problems continue in adulthood). From an initial pool of 171 relevant studies, 15 studies met the inclusion criteria, with eight published articles and seven dissertations yielding 48 effect sizes, N Total = 16,239). The overall effect size was significant and of medium magnitude (d = 0.51, p < .001). Results from the study suggest support for the continuance hypothesis, with rates of adult internalizing problems similar to those found in studies of children and adolescents with LD. Moderating variables are explored, and implications for practice and research are offered.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.082
GPT teacher head0.363
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations94
Published2011
Admission routes1
Has abstractyes

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