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Record W4306377440 · doi:10.3390/disabilities2040043

Psychopathology among Emerging Adults with Learning Disabilities in Canada

2022· article· en· W4306377440 on OpenAlexaffabout
Samantha Leslie Chown, Dillon T. Browne, Scott T. Leatherdale, Mark A. Ferro

Bibliographic record

VenueDisabilities · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPsychosocialMental healthPsychopathologyLogistic regressionOddsDistressClinical psychologyYoung adultPsychological distressPsychologyPsychiatryMedicineGerontology

Abstract

fetched live from OpenAlex

Individuals with learning disabilities (LDs) are more likely to have a mental illness, yet few studies explore this association in emerging adulthood, a developmental period with an increased risk for mental illness. The purpose of the current study was to investigate psychological distress in emerging adults (15–29 years) with and without LDs. The 2012 Canadian Community Health Survey—Mental Health was used (n = 5630), and multiple and logistic regression models with survey weights were computed. Adjusting for demographic, psychosocial, and health covariates, there was no evidence for significant differences in psychological distress among emerging adults with vs. without LDs. However, age and sex were significant effect modifiers. Among emerging adults with LDs, both males (OR = 2.39 [1.01, 5.67]) and those aged 25–29 years (OR = 3.87 [1.05, 14.30]) had an increased odds of clinically relevant psychological distress in comparison to those without LDs. These findings suggest a need for improved awareness and support for prevention of psychological distress among emerging adults with LDs, especially for males and those in later emerging adulthood.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.017
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
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 head0.026
GPT teacher head0.339
Teacher spread0.313 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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