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Record W3039920265 · doi:10.1111/jar.12775

Using a think‐aloud methodology to understand online physical activity information search experiences and preferences of parents of children and youth with disabilities

2020· article· en· W3039920265 on OpenAlexaff
Tharsheka Natkunam, Lauren Tristani, Danielle Peers, Jessica Fraser‐Thomas, Amy E. Latimer‐Cheung, Rebecca Bassett‐Gunter

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsQueen's UniversityUniversity of AlbertaYork University
Fundersnot available
KeywordsThink aloud protocolPsychologyThe InternetAffect (linguistics)Information seekingOnline searchRead aloudApplied psychologyMedical educationComputer scienceWorld Wide WebMedicineInformation retrievalCommunicationPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The Internet is a preferred source of physical activity (PA) information. However, limited research exists regarding the experiences of parents of children and youth with disabilities (CYWD) in searching for PA programme information online. This research examined the experiences and preferences of parents of CYWD in searching for PA programme information online. METHOD: Parents of CYWD (n = 10) participated in a think-aloud exercise while searching for PA programme information online. Following the think-aloud exercise, semi-structured interviews were used to further understand parents' experiences and preferences in searching for PA programme information online. RESULTS: Parents identified key features that contributed to a positive online search experience. Additionally, parents noted challenges and resulting negative affect that was experienced. CONCLUSIONS: This research can inform the development and dissemination of online PA programme information that is accessible and relevant to the preferences of parents of CYWD and can facilitate positive search experiences.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.393
GPT teacher head0.473
Teacher spread0.079 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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