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Record W2888041916 · doi:10.2196/11058

Exploring the Needs of Adolescents With Sickle Cell Disease to Inform a Digital Self-Management and Transitional Care Program: Qualitative Study

2018· article· en· W2888041916 on OpenAlexaffvenue
Yalinie Kulandaivelu, Chitra Lalloo, Richard Ward, William T. Zempsky, Melanie Kirby‐Allen, Vicky R. Breakey, Isaac Odame, Fiona Campbell, Khush Amaria, Ewurabena Simpson, Cynthia Nguyen, Tessy George, Jennifer Stinson

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2018
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversity of OttawaMcMaster UniversityMcMaster Children's HospitalUniversity of TorontoSickKids FoundationUniversity Health NetworkChildren's Hospital of Eastern OntarioInstitute for Clinical Evaluative SciencesHospital for Sick Children
Fundersnot available
KeywordsPsychological interventionSelf-managementDisease managementDiseaseHealth careMedicineQuality of life (healthcare)PhonePopulationQualitative researchGerontologyNursingPsychologyFamily medicineEnvironmental healthComputer sciencePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Accessible self-management interventions are critical for adolescents with sickle cell disease to better cope with their disease, improve health outcomes and health-related quality of life, and promote successful transition to adult health care services. However, very few comprehensive self-management and transitional care programs have been developed and tested in this population. Internet and mobile phone technologies can improve accessibility and acceptability of interventions to promote disease self-management in adolescents with sickle cell disease. OBJECTIVE: The aim of this study was to qualitatively explore the following from the perspectives of adolescents, parents, and their health care providers: (1) the impact of sickle cell disease on adolescents to identify challenges to their self-management and transitional care and (2) determine the essential components of a digital self-management and transitional care program as the first phase to inform its development. METHODS: A qualitative descriptive design utilizing audio-recorded, semistructured interviews was used. Adolescents (n=19, aged 12-19 years) and parents (n=2) participated in individual interviews, and health care providers (n=17) participated in focus group discussions and were recruited from an urban tertiary care pediatric hospital. Audio-recorded data were transcribed verbatim and organized into categories inductively, reflecting emerging themes using simple content analysis. RESULTS: Data were categorized into 4 major themes: (1) impact of sickle cell disease, (2) experiences and challenges of self-management, (3) recommendations for self-management and transitional care, and (4) perceptions about a digital self-management program. Themes included subcategories and the perspectives of adolescents, parents, and health care providers. Adolescents discussed more issues related to self-management, whereas health care providers and parents discussed issues related to transition to adult health services. CONCLUSIONS: Adolescents, parents, and health care providers described the continued challenges youth with sickle cell disease face in terms of psychosocial impacts and stigmatization. Participants perceived a benefit to alleviating some of these challenges through a digital self-management tool. They recommended that an effective digital self-management program should provide appropriate sickle cell disease-related education; guidance on developing self-advocacy and communication skills; empower adolescents with information for planning for their future; provide options for social support; and be designed to be engaging for both adolescents and parents to use. A digital platform to deliver these elements is an accessible and acceptable way to address the self-management and transitional care needs of adolescents.

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)0.007
metaresearch head score (Gemma)0.007
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 score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.022
GPT teacher head0.287
Teacher spread0.265 · 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".

Quick stats

Citations38
Published2018
Admission routes2
Has abstractyes

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