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Record W1995890206 · doi:10.1007/s10897-008-9151-6

Experiences of Teens Living in the Shadow of Huntington Disease

2008· article· en· W1995890206 on OpenAlexaboutno aff
Kathleen J.H. Sparbel, Martha Driessnack, Janet K. Williams, Debra L. Schutte, Toni Tripp‐Reimer, Meghan McGonigal‐Kenney, Lori Jarmon, Jane S. Paulsen

Bibliographic record

VenueJournal of Genetic Counseling · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeU.S. Public Health ServiceNational Institutes of Health
KeywordsGenetic counselingFocus groupQualitative researchCoping (psychology)DiseaseHealth carePsychologyMedicineHuntington's diseaseGerontologyClinical psychologyDevelopmental psychologySociologyGenetics

Abstract

fetched live from OpenAlex

Research on families with Huntington Disease (HD) has primarily focused on adult decision-making surrounding predictive genetic testing and caregiver stress. Little is known about the experiences of teens living in these families. This qualitative study explored the experiences of 32 teens living in families with HD. Six focus groups were conducted across the U.S. and Canada. Data were analyzed using descriptive qualitative analysis. Huntington disease appeared to cast a shadow over the experiences described by teens. Four themes were identified: watching and waiting; alone in the midst of others; family life is kind of hard; and having to be like an adult. These experiences highlight the need for genetic counselors, health care providers, and school personnel to be aware of issues facing teens living in families with HD. Recognizing patterns of teen experiences may help health care providers develop strategies to support coping by teens in HD families.

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)0.003
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 score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.003
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 head0.023
GPT teacher head0.282
Teacher spread0.260 · 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

Citations49
Published2008
Admission routes1
Has abstractyes

Explore more

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