MétaCan
Menu
Back to cohort
Record W3044866728 · doi:10.1111/hex.13109

Is it worth it?: The experiences of persons with multiple sclerosis as they access health care to manage their condition

2020· article· en· W3044866728 on OpenAlexaff
Julie Pétrin, Catherine W. Donnelly, MaryAnn McColl, Marcia Finlayson

Bibliographic record

VenueHealth Expectations · 2020
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsBiopsychosocial modelHealth carePsychologyFocus groupNursingMedicinePsychiatryBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: People with multiple sclerosis (MS) require complex care throughout life. Canadians with MS are high users of health-care services, yet still report unmet health-care needs and low satisfaction with services received. OBJECTIVE: This study aimed to investigate the health-care access experiences of Ontarians with MS as they manage their condition. DESIGN AND PARTICIPANTS: Interpretive description guided data collection and analysis. Forty-eight people living across seven communities participated. Thirty-eight participated in one of five focus groups; the remaining 10 participated in an individual semi-structured interview. RESULTS: Participants described the experience of accessing care as a decisional process, guided by a form of cost-benefit analysis. The process determined whether seeking conventional health-care services 'is worth it'. Most participants felt that the energy and resources required to access the health-care system outweighed their expected outcomes, based on past experiences. Participants who did not see the benefit of care seeking turned to self-treatment, use of complementary and alternative services, and engaged in patterns of health-care avoidance until a crisis arose. DISCUSSION AND CONCLUSION: Findings suggest that a renewed effort to promote patient-centred care and a biopsychosocial approach may improve the health-care access experiences of persons with MS and reduce service avoidance.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.189
GPT teacher head0.422
Teacher spread0.233 · 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 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

Citations33
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueHealth ExpectationsSame topicMultiple Sclerosis Research StudiesFrench-language works237,207