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Record W4385556350 · doi:10.1111/cch.13156

Transition Readiness Assessment Questionnaire: Skill gaps and psychosocial predictors of transition readiness among adolescents and young adults with chronic medical conditions

2023· article· en· W4385556350 on OpenAlexaffabout
Pascale Chapados, Sabrina Provencher, Jennifer Aramideh, Émilie Dumont, Tziona Lugasi, Caroline Laverdière, Serge Sultan, Léandra Desjardins

Bibliographic record

VenueChild Care Health and Development · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsPsychosocialYoung adultLikert scaleQuality of life (healthcare)PsychologyAdult careLogistic regressionClinical psychologyMedicineTracking (education)Health careFamily medicinePhysical therapyGerontologyPsychiatryNursingDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Transferring from paediatric to adult care can be challenging. Adolescents and young adults (AYAs) with chronic health conditions need to develop a specific set of skills to ensure lifelong medical follow-up due to the chronicity of their condition. The Transition Readiness Assessment Questionnaire-French version (TRAQ-FR) is a 19-item questionnaire measuring such skills. The aims of the study were to (1) describe participant characteristics and (2) identify constructs related to, and predictors of, having learned domain-specific transition readiness skills. METHODS: Participants included 216 AYAs aged 14-20 years (M = 15.93; SD = 1.35; 54.1% male) recruited from five outpatient clinics in a Canadian tertiary hospital. AYAs completed the TRAQ-FR, the Pediatric Quality of Life Inventory 4.0 (PedsQL) and a sociodemographic questionnaire. Descriptive, bivariate and binary logistic regression analyses were conducted. RESULTS: Overall, participants reported significantly higher scores on the Talking with Providers, Managing Daily Activities and Managing Medications subscales than on the Appointment Keeping and Tracking Health Issues subscales (F[41075] = 168.970, p < .001). At the item level, median scores (on a 5-point Likert scale) suggest that AYAs had begun practising five of the 19 skills (median scores ≥4; 'Yes, I have started doing this'), while a median score of 1 ('No, I don't know how') was found for one item ('Do you get financial help with school or work?'). At the subscale level, TRAQ-FR skills and skill gaps were related to AYAs' age, sex and PedsQL scores (ps < .05). CONCLUSION: Older and female AYAs were more likely to have begun practising specific TRAQ-FR subscale skills. Better psychosocial functioning was also related to having learned specific transition readiness skills. AYAs show several gaps in transition readiness. Targeted intervention in transition readiness skill development could take into account AYAs' age, sex and psychosocial functioning for a successful transfer to adult care.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.011
GPT teacher head0.345
Teacher spread0.333 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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