MétaCan
Menu
Back to cohort
Record W4402676098 · doi:10.1097/nsg.0000000000000076

Barriers to the health and well-being of women with multiple sclerosis in Southwestern Ontario, Canada

2024· article· en· W4402676098 on OpenAlexaffabout
Jennifer Collins, Yolanda Babenko‐Mould, Kimberley T. Jackson, Tracy Smith‐Carrier

Bibliographic record

VenueNursing · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMemorial University of NewfoundlandWestern University
Fundersnot available
KeywordsFeelingLived experienceQualitative researchMental healthFinancial securityWell-beingPsychologyGerontologyMedicineSociologyPsychiatrySocial psychologyBusinessPsychotherapist

Abstract

fetched live from OpenAlex

PURPOSE: This study explores the lived experiences of women living with multiple sclerosis (MS) and identifies barriers to their optimal health and well-being. METHODOLOGY: Using van Manen's interpretative phenomenologic analysis, the researchers conducted semistructured interviews with 23 women diagnosed with MS in Southwestern Ontario, Canada. Data were analyzed using NVivo 12 software, and themes were validated through member checking. RESULTS: The study revealed a key theme of "obstacles for women with MS" and subthemes related to barriers to physical, mental, and social well-being. Participants reported experiencing feelings of health despite their MS diagnosis but identified various constraints on their optimal health and well-being, including challenges with employment, financial support, and housing security. CONCLUSION: The findings highlight the need for healthcare professionals to advocate for equitable treatments, medication, and accessibility supports for women with MS, as well as for policies that address disability income support and affordable housing. Further research is recommended to explore power imbalances experienced by women with MS in precarious employment situations or living with episodic disabilities.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.273
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.027
GPT teacher head0.281
Teacher spread0.254 · 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.

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 routes2
Has abstractyes

Explore more

Same venueNursingSame topicMultiple Sclerosis Research StudiesFrench-language works237,207