MétaCan
Menu
Back to cohort
Record W2789654625 · doi:10.1097/sga.0000000000000288

Identifying Needs in Young Adults With Inflammatory Bowel Disease

2018· article· en· W2789654625 on OpenAlexafffund
Romy Cho, Natasha Wickert, Anne F. Klassen, Elena Tsangaris, John K. Marshall, Herbert Brill

Bibliographic record

VenueGastroenterology Nursing · 2018
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsMcMaster Divinity CollegeMcMaster University
FundersCanadian Institutes of Health Research
KeywordsPsychosocialYoung adultMedicinePsychological interventionInflammatory bowel diseaseDiseaseQuality of life (healthcare)GerontologyHealth careFamily medicinePsychiatryNursingInternal medicine

Abstract

fetched live from OpenAlex

Successful transitional care for young adults entails that healthcare teams recognize and understand the attitudes, perspectives, and developmental maturity of young adults. The aim of this study was to identify the needs of young adults with inflammatory bowel disease. Young adults 18-30 years of age were recruited from the McMaster University Medical Centre adult inflammatory bowel disease (IBD) clinic between July 2012 and May 2013. Semistructured interviews were audio taped, transcribed verbatim, and coded using a constant comparative method. QSR NVivo10 software was used to manage the data. Twenty-one young adults, including 15 subjects diagnosed as adolescents (younger than 18 years) and 6 subjects diagnosed as young adults, were interviewed. Four broad categories of needs were identified: psychosocial, informational, self-advocacy, and daily living needs. The most commonly reported needs were psychosocial and the least common were daily living needs. Results from this study may be used to inform clinical practitioners of potential needs that may be important to the overall quality of patient health during young adulthood. In addition, these findings may be used to evaluate existing transition and self-management tools to measure success of transition interventions more effectively.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.361
Teacher spread0.335 · 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.

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

Citations25
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueGastroenterology NursingSame topicAdolescent and Pediatric HealthcareFrench-language works237,207