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Record W2617618556 · doi:10.1080/17518423.2017.1323969

Exploring the ICF-CY as a framework to inform transition programs from pediatric to adult healthcare

2017· article· en· W2617618556 on OpenAlexaff
Laura R. Hartman, Amy C. McPherson, Joanne Maxwell, Sally Lindsay

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2017
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalPublic Health OntarioToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsInternational Classification of Functioning, Disability and HealthSpina bifidaAdult careHealth carePsychologyTransition (genetics)Transitional careGerontologyDevelopmental psychologyMedical educationYoung adultMedicineClinical psychologyRehabilitationPediatrics

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the utility of the International Classification of Functioning, Disability and Health-Children and Youth Version (ICF-CY) for informing transition-related programs for youth with chronic conditions moving into adult healthcare settings, using an exemplar spina bifida program. METHODS: Semi-structured in-depth interviews were conducted with 53 participants (9 youth and 11 parents who participated in a spina bifida transition program, 12 young adults who did not, 12 clinicians, and 9 key informants involved in development/implementation). Interview transcripts were thematically analyzed, and then further coded using ICF-CY domain codes. RESULTS: ICF-CY domains captured many key areas regarding individuals" transitions to adult care and adult functioning, but did not fully capture concepts of transition program experience, independence, and parents" role. CONCLUSIONS: The ICF-CY framework captures some experiences of transitions to adult care, but should be considered in conjunction with other models that address issues outside of the domains covered by the ICF-CY.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.189
GPT teacher head0.425
Teacher spread0.236 · 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 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

Citations11
Published2017
Admission routes1
Has abstractyes

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