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Record W3043263156 · doi:10.2196/16367

Social Media Use Among Young Adults With Connective Tissue Disorders: Cross-Sectional Pilot Study

2020· article· en· W3043263156 on OpenAlexvenueno aff
Erin Kelleher, Philip F. Giampietro, Megan A. Moreno

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
FundersMarfan FoundationUniversity of Wisconsin-Madison
KeywordsSocial mediaMarfan syndromeSocial supportMedicineHealth carePerceptionPsychologyFamily medicineSurgerySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Young people with genetic conditions often face challenges coping with their health condition. It can be difficult for them to meet someone with a similar condition, which is important for reinforcement of chronic illness management recommendations. Social media is used by 97% of young people in the United States and may provide those with these disorders a space for emotional expression and support. However, there is a scarcity of literature related to the use of social media among adolescents with genetic conditions as an indicator of their perception regarding their own condition. OBJECTIVE: The purpose of this pilot study was to obtain preliminary data to assess and understand social media use by young people with connective tissue disorders and determine whether they use social media to connect with patients with similar conditions or whether they would be interested in doing so. METHODS: We undertook a pilot study of selected connective tissue disorders occurring in young people between the ages of 11 and 25 years, including Marfan syndrome; Ehlers-Danlos syndrome subtypes classical, classical-like, cardiac-valvular, and vascular; Beals congenital contractual arachnodactyly; and Alport hereditary nephritis. The study took place within one pediatric clinical system. Patients were identified through electronic medical record search and International Classification of Diseases, Ninth Revision, coding at a Midwest university-based clinical system. Study subjects completed a short survey describing their experiences with their connective tissue disorders, their means of self-expression, their existing network of persons to communicate with, and their use of social media. Data analysis included nominal and bivariate regressions to compare social media use in relation to age. RESULTS: Our 31 participants (42% response rate) were 55% female (17/31) and their average age was 18 years (SD 5). All participants used social media and there were no statistically significant differences between social media use and age. The majority of participants (25/30, 83%) reported that they never used social media to discuss their condition (P=.09), and only 17% (5/30) knew someone online with a similar condition (P=.50). Most participants (19/30, 63%) said they would communicate with someone with a similar disorder (P=.64). CONCLUSIONS: We found that young individuals with connective tissue disorders use at least one type of social media. A majority did not use social media to discuss their condition or know someone online with a similar condition. However, many persons were interested in finding others similarly affected. Social media could serve as a platform for young people with connective tissue disorders to connect. Peer support is important in disease management and adolescent development. Future studies should aim at understanding social media use among young people with connective tissue disorders and helping them connect with other people who have similar conditions.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.110
GPT teacher head0.391
Teacher spread0.281 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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