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Record W4239368295 · doi:10.1093/ibd/izy393.000

06 FOCUSING ON THE FUTURE: REDUCING BARRIERS AND IMPROVING ACCESS TO IBD SPECIALTY CARE

2019· article· en· W4239368295 on OpenAlexaboutno aff
Courtney Heisler, Olga Kits, Sander Veldhuyzen van Zanten, Jennifer M. Jones

Bibliographic record

VenueInflammatory Bowel Diseases · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFocus groupSpecialtyQualitative researchFamily medicineHealth careThematic analysisGerontologyPolitical science

Abstract

fetched live from OpenAlex

Inflammatory Bowel Disease (IBD) is a chronic, immune-mediated disease that affects approximately two million North Americans. Canada has the highest age-adjusted incidence and prevalence rates of IBD globally. Given its cumulative prevalence, the IBD clinical burden in North America continues to grow. Limitations in accessing specialty healthcare services is not a new issue facing patients and healthcare providers. Despite this persistent problem, no research elucidating the patient perspective using qualitative approaches to compare and contrast the patient experience across diverse geographic regions has been conducted. The aim of the current study was to elicit a qualitative stream of data to better understand phenomena related to access to healthcare for Canadians living with IBD from a patient-centered perspective. IBD patients (≥18 years of age) were recruited from gastroenterology clinics and communities through IBD specialists and Crohn’s & Colitis Canada. Patients were recruited from both urban and rural locales to ensure adequate representation from geographically diverse regions. Focus groups provided a powerful and more naturalistic tool through which a focused understanding of the patient experience was derived. Co-facilitated by a researcher and a patient research partner, the focus groups were held in Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia. Patient demographics were collected to contextualize observed themes. Themes were distilled through qualitative thematic analysis using Atlas.ti software to ascertain congruence or discordance of patient experiences. Eastern Canadian focus groups have been completed, with recruitment underway for the Western Canadian focus groups. A total of 20 participants were recruited as of October 2018. The majority of participants were male (11/20, 55%) and were from urban/suburban regions (10/20, 50%). The mean age of participants was 44 years of age (SD=12 years, range=24 to 67 years). Preliminary analyses show that the main patient-identified barriers to accessing IBD care fall into the following categories: 1) Lack of multidisciplinary care (including psycho-social support), 2) Diagnostic delay, and 3) Inability to effectively receive and provide communication with healthcare providers. Solutions identified by patients included: 1) Integration of more holistic care into the clinical practice, 2) Readily accessible psychiatric and nutritional support, 3) Increased patient advocacy, and 4) Continuity and liaison through provision of a healthcare navigator resource. Healthcare access for IBD patients is complex. It is important to not only have a robust understanding of the healthcare system structure and processes but also the significant impact these factors have on patients. Access improvement research can be best tackled through patient-centered exploration of themes related to access to care.

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)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.513
Threshold uncertainty score0.980

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0170.006
Scholarly communication0.0080.004
Open science0.0020.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0150.002

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 head0.006
GPT teacher head0.231
Teacher spread0.226 · 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 designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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