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Record W2892939675 · doi:10.15453/2168-6408.1455

Concept Mapping and the CO-OP Approach with Adolescents with Autism Spectrum Disorder: Exploring Participant Experiences

2018· article· en· W2892939675 on OpenAlexaff
Jessie Wilson, Angela Mandich, Lílian Magalhães, Kaity Gain

Bibliographic record

VenueThe Open Journal of Occupational Therapy · 2018
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsWestern University
Fundersnot available
KeywordsAutism spectrum disorderOccupational therapyIntervention (counseling)PsychologyAutismClinical psychologyDevelopmental psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Background: To explore the experiences of adolescents with ASD participating in a novel occupationally based intervention. Methods: The intervention used concept mapping in combination with the CO-OP approach with 10 adolescents with ASD in a 4-week program focused on developing life skills they deemed as important in their transition to adulthood. A descriptive qualitative approach was employed using deductive thematic analysis informed by Self-Determination Theory and occupationally relevant theoretical frameworks. This study is part of a larger feasibility project and focuses on the analysis of participant reflections and researcher field notes. Results: Five themes emerged: finding a sense of balance through negotiating tensions; a sense of “we” and a sense of “I”; selecting purposeful, meaningful, and authentic occupations; multimodal tools; and action through participating in doing. Conclusion: This study highlights valuable participant insights into their involvement in a novel occupationally based intervention that will inform the program’s on-going development and implementation.

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)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0030.003
Open science0.0020.008
Research integrity0.0020.003
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 head0.253
GPT teacher head0.382
Teacher spread0.130 · 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 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

Citations7
Published2018
Admission routes1
Has abstractyes

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