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Record W4375948708 · doi:10.18666/jorel-2022-11350

Happy Campers: Enhancing Social Competence in Adolescents with Attention-Deficit/Hyperactivity Disorder at Summer Camp

2023· article· en· W4375948708 on OpenAlexaff
Kirsten Neprily, Emma A. Climie

Bibliographic record

VenueJournal of Outdoor Recreation Education and Leadership · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychosocialSummer campSocial competenceSocial skillsPsychologyAttention deficit hyperactivity disorderCompetence (human resources)Developmental psychologyNormativeClinical psychologySocial changePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Many adolescents with attention-deficit/hyperactivity disorder (ADHD) experience challenges in social competence. Evidence suggests that specialized summer camps with social skills training may have positive outcomes on social competence development in adolescents with ADHD. This article reports on a pilot study of a therapeutic summer camp program for children and adolescents with ADHD. The study examines the degree to which program objectives were achieved through a pre-camp, post-camp design using a series of standardized instruments, camp evaluations, and surveys with parents. The results indicated that the campers initially reported significantly lower social competence when compared to a normative sample of adolescents before the camp but improved their social competence by the end of camp. This research has implications for researchers, caregivers, and outdoor education advocates to take an assertive lead in promoting evidence-based psychosocial programs for youth with and without ADHD into more generalizable community settings.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.091
GPT teacher head0.341
Teacher spread0.250 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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Same venueJournal of Outdoor Recreation Education and LeadershipSame topicYouth Development and Social SupportFrench-language works237,207