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Record W3087119808 · doi:10.1177/1055665620957215

Online Cleft Educational Videos: Parent Preferences

2020· article· en· W3087119808 on OpenAlexaff
Karina Spoyalo, Rebecca Courtemanche, Erika Henkelman

Bibliographic record

VenueThe Cleft Palate-Craniofacial Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMultidisciplinary approachFocus groupTrustworthinessMedicineMedical educationPsychologyFamily medicineSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: Determine what parents of children with cleft lip and palate value in online educational videos and evaluate whether their needs are currently being met. DESIGN: Focus groups and telephone interviews were used to define parent information needs, followed by an evaluation of whether currently available YouTube videos meet these needs. SETTING: British Columbia Children's Hospital multidisciplinary cleft clinic. PARTICIPANTS: Twenty-four parents of children with nonsyndromic cleft lip and palate. RESULTS: Parents desired videos that are accessible, trustworthy, relatable, and positive. Parents preferred a series of short videos addressing relevant topics as their child grows. Currently available YouTube videos only partially met these needs, with underrepresented topics including hearing, dentition, and surgeries for older children. CONCLUSIONS: While access and validity of video resources can be improved by directing patients and families to appropriate videos, some parent needs remain unmet.

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)0.002
metaresearch head score (Gemma)0.013
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.108
GPT teacher head0.433
Teacher spread0.326 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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