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Brief report: An online support intervention: Perceptions of adolescents with physical disabilities

2010· article· en· W2132462496 on OpenAlexafffund
Moira Stewart, Alison Barnfather, Joyce Magill‐Evans, Lynne Ray, Nicole Letourneau

Bibliographic record

VenueJournal of Adolescence · 2010
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of New BrunswickUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta Heritage Foundation for Medical Research
KeywordsLonelinessPsychologyCerebral palsyIntervention (counseling)Spina bifidaCoping (psychology)Social supportPsychological interventionClinical psychologyQualitative researchDevelopmental psychologyMedicinePsychiatrySocial psychologyPediatrics

Abstract

fetched live from OpenAlex

Adolescents with cerebral palsy and spina bifida report restricted interactions with peers and gaps in social support. A pilot online support intervention offered interactions with peers. Five mentors with cerebral palsy or spina bifida and 22 adolescents with the same disabilities met weekly online for 25 group sessions over six months. Participants completed quantitative measures of loneliness, sense of community, self-perceptions, coping, and social support prior to intervention, post-intervention, and delayed post-intervention. Semi-structured qualitative interviews elicited perceptions of the intervention's impacts. Participants reported more contact with teens with disabilities, decreased loneliness, and increased social acceptance and confidence. A significant increase in sense of community was reported from post-intervention to delayed post-intervention. Encouraging qualitative findings were supported by trends in the quantitative measures. This pilot study can guide a future community-based intervention trial.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.040
GPT teacher head0.383
Teacher spread0.343 · 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

Citations65
Published2010
Admission routes2
Has abstractyes

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