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Record W2922835156

Using a think aloud methodology to understand physical activity Internet search experiences and preferences of parents of children/youth with disabilities

2018· article· en· W2922835156 on OpenAlexaff
Tharsheka Natkunam, Danielle Peers, Amy E. Latimer‐Cheung, Rebecca Bassett‐Gunter

Bibliographic record

VenueYorkSpace (York University) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsUniversity of AlbertaYork University
Fundersnot available
KeywordsThink aloud protocolThe InternetPsychologyThematic analysisInformation needsInformation seekingApplied psychologyQualitative researchInternet privacyWorld Wide WebComputer scienceUsabilityInformation retrievalSociology
DOInot available

Abstract

fetched live from OpenAlex

This research examined the physical activity (PA) internet search experiences and preferences of parents of children/youth with disability (CYWD). A sample of parents of CYWD (n=10) participated in a prompted think aloud process (i.e., verbalize thoughts) while searching for PA information online. Researchers observed the parents and gathered information regarding their experience and preferences. Using an inductive thematic analysis of the parents think aloud responses, the following emerged as key themes regarding online PA information needs: Know exactly what programs they offer, Keep it very very simple, and More work for parents to find something. Parents used as an online evaluation criterion, including information parents considered important, to determine the suitability of the program for their CYWD. An improved understanding of parents experiences and preferences while searching for PA information can inform how PA or disability organizations structure their websites to create 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 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)0.005
metaresearch head score (Gemma)0.009
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 score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.197
GPT teacher head0.379
Teacher spread0.183 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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