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Record W3180459776 · doi:10.1177/20552173211029672

Prioritizing the healthcare access concerns of Canadians with MS

2021· article· en· W3180459776 on OpenAlexaff
Julie Pétrin, Mary Ann McColl, Catherine Donnelly, Simon French, Marcia Finlayson

Bibliographic record

VenueMultiple Sclerosis Journal - Experimental Translational and Clinical · 2021
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsHealth careDescriptive statisticsQuality (philosophy)BusinessMedicineNursingFamily medicinePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Canadians with MS are high users of healthcare services, yet they report multiple unmet needs, high disease burden, and low satisfaction with care. Engaging patients in healthcare planning can lead to improvements in access and care. There is currently limited evidence that has harnessed the perspectives of Canadians with MS. OBJECTIVE: To identify and prioritize the healthcare access concerns of Canadians with MS. METHODS: A cross-sectional online survey informed by the Concerns Report Methodology was used to address the objective. Participants were recruited through multiple methods. Descriptive statistics were used to identify the main barriers to healthcare providers, and concerns report methods were used to calculate needs indexes to prioritize concerns of participants. RESULTS: 324 Canadians with MS participated in the study between November 18, 2019 and March 27, 2020. The most pressing healthcare access concerns of Canadians with MS were related to availability of healthcare providers with MS knowledge and affordability of services that aim to improve wellness. CONCLUSION: These findings provide healthcare planners with prioritized access concerns of Canadians with MS, which can be used to guide strategic planning to improve the quality of life of these individuals.

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)0.001
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 score0.039
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.277
GPT teacher head0.440
Teacher spread0.163 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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