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Record W4411334719 · doi:10.1111/jar.70077

Thriving for Adolescents With Intellectual Disability: A Photo‐Elicitation Qualitative Study

2025· article· en· W4411334719 on OpenAlexafffund
Annie S. Mills, Teresa Sellitto, Dallas D. Sorken, Katie Saunders, Lauren Bishop, Jan Willem Gorter, Jonathan A. Weiss

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2025
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsMcMaster UniversityYork University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsThrivingThematic analysisIntellectual disabilityQualitative researchPsychologyPhoto elicitationReflexivityDevelopmental psychologyQualitative analysisPsychiatrySociologyPsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: There has been a shift in recent decades towards studying the strengths of people with intellectual disability to promote wellbeing. This study developed a conceptualisation of thriving specific to adolescents with intellectual disability. METHOD: A photo-elicitation qualitative design was used. Participants were 12 adolescents (ages 12-19) with a diagnosis of intellectual disability and their parents. Adolescents and their parents separately chose photos demonstrating thriving for the adolescent. Semi-structured interviews were conducted. RESULTS: Reflexive thematic analysis was used. Five core themes that interact and contribute to thriving were developed: Enjoying Life, Developing, Having a Positive Sense of Self, Connecting, and Mattering. Parents also identified the importance of adolescents being Safe & Supported. CONCLUSIONS: This thriving framework can serve as a guiding template for community supports and future quantitative research studies for adolescents with intellectual disability.

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)0.013
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.162
GPT teacher head0.493
Teacher spread0.331 · 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.

Study designQualitative
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

Citations1
Published2025
Admission routes2
Has abstractyes

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