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Record W2119274094 · doi:10.1177/1049732303259618

Health Care Communication Issues in Multiple Sclerosis: An Interpretive Description

2004· article· en· W2119274094 on OpenAlexaff
Sally Thorne, Andrea Con, Liza McGuinness, Gladys McPherson, Susan R. Harris

Bibliographic record

VenueQualitative Health Research · 2004
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHealth careHealth communicationSet (abstract data type)Qualitative researchMedicineFocus groupPsychologyNursingSociologyCommunicationComputer science

Abstract

fetched live from OpenAlex

Communication between persons with chronic illness and their professional health care providers is a critical element of appropriate health care. As the field of health care communication evolves, it becomes apparent that aspects of the illness experience shared by those affected by specific diseases might be a source of particular insight into what constitutes effective or appropriate communications. This interpretive description of health care communication issues in multiple sclerosis was based on qualitative secondary analysis of a set of in-depth interviews and focus groups conducted with 12 persons with longstanding MS experience. Analysis of their accounts illustrates an intricate interplay between common features within the disease trajectory and the communications that are perceived as helpful or unhelpful to living well with this chronic illness. From the analysis of these findings, the authors draw interpretations regarding what might be considered communication competencies for those who care for patients with this disease.

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)0.019
metaresearch head score (Gemma)0.028
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 score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0110.029
Scholarly communication0.0120.008
Open science0.0030.007
Research integrity0.0040.005
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 head0.602
GPT teacher head0.602
Teacher spread0.000 · 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

Citations164
Published2004
Admission routes1
Has abstractyes

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