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Record W2112418751 · doi:10.1177/1049732314544966

Navigating Life and Loss in Pediatric Multiple Sclerosis

2014· article· en· W2112418751 on OpenAlexaff
Jennifer E. Thannhauser

Bibliographic record

VenueQualitative Health Research · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGrounded theoryGriefIntrapersonal communicationMultiple sclerosisInterpersonal communicationPsychologyDiseaseDevelopmental psychologyPerspective (graphical)MedicinePsychotherapistQualitative researchPsychiatrySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) is a chronic disease of the central nervous system that can cause unpredictable disability. Over the past 10 to 15 years, practitioners and researchers have come to recognize that children and adolescents are at risk for this disease. Drawing on the experiences of pediatric MS patients and their parents, I designed this study to explicate the process of adjustment to the disease. Using Charmaz's constructivist grounded theory methodology, I developed a preliminary theory that captures the experience of grief in the adjustment process of young people with MS. The core of the theoretical model focuses on two separate, yet overlapping processes: recurring loss and carrying on. Significant turning points influenced the oscillation between these two processes, highlighting the interconnection of intrapersonal and interpersonal dynamics in adjustment to the disease. Results reinforce and extend current grief literature and provide an alternative perspective on adjustment to pediatric chronic illness.

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)0.004
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 score0.004
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.459
GPT teacher head0.568
Teacher spread0.108 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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