MétaCan
Menu
Back to cohort
Record W4399771368 · doi:10.32920/26052907

Couples Coping With Multiple Sclerosis: Does Dyadic Coping Congruence Matter?

2024· preprint· en· W4399771368 on OpenAlexaff
Kaitlin McGarragle

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsToronto Metropolitan UniversityYork UniversityUniversity of Toronto
Fundersnot available
KeywordsCoping (psychology)Congruence (geometry)PsychologyClinical psychologyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) affects 90,000 Canadians and 25% of MS patients report their relationship is negatively affected by MS. Patient and partner wellbeing are also correlated therefore, examining coping from a dyadic perspective is warranted. The objectives of this thesis were to explore individual and conjoint dyadic coping (DC) strategies and DC congruence in couples high and low in relationship functioning (RF). MS patients and their partners completed quantitative measures and purposive sampling was used to categorize couples as high or low in RF. Interviews were conducted with participants separately and results were analyzed using thematic analysis. High RF couples generally engaged in adaptive strategies (e.g., illness acceptance) and low RF couples generally engaged in maladaptive strategies (e.g., conceptualizing MS as an individual issue). DC congruence was not as important as the additive effect of positive DC strategies. Contextual factors like life stage, financial stress, and culture influenced DC strategies.

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)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.286
Teacher spread0.246 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicFamily Support in IllnessFrench-language works237,207