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Record W2927526067

Arts Express: An Adapted Creative Arts Camp in Waterloo, Ontario

2019· article· en· W2927526067 on OpenAlexaffabout
Nicole Reinders, Brianne Redquest, Paula C. Fletcher, Margaret A. Schneider, Pamela J. Bryden

Bibliographic record

VenuePALAESTRA · 2019
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsCentre for Addiction and Mental HealthWilfrid Laurier University
Fundersnot available
KeywordsRespite careThe artsDanceDramaPerforming arts educationPsychologyPublic relationsArts in educationVisual artsPedagogySociologyMedical educationNursingMedicinePolitical scienceArt
DOInot available

Abstract

fetched live from OpenAlex

Children with additional needs (CwAN) face barriers when accessing resources such as summer camps due to limited program accessibility. Full-day camps are a form of respite care for primary caregivers, while also providing a sense of independence for children. The arts are known to offer psychological benefits when explored in a creative and inclusive environment. This article describes a creative arts camp for CwAN from the perspective of two camp counselors and eight primary caregivers of CwAN. The camp details are described, from counselor training to the final performance at the end of the camp week. Caregivers felt that this camp structure allowed their CwAN to gain worth-while experiences in dance, drama, music, and visual arts. It also allowed them to feel a sense of safety and belonging by spending time with other CwAN and supportive camp counselors and volunteers. There are many strengths of this camp structure that can be applied to other camps in other settings. Program planners and community organizations should consider incorporating the creative arts into existing and developing programs.Subscribe to PALAESTRA

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)0.001
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 score0.179
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0140.002
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0390.002

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 head0.064
GPT teacher head0.343
Teacher spread0.279 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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Same venuePALAESTRASame topicFamily and Disability Support ResearchFrench-language works237,207