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Record W4233120422 · doi:10.1093/ibd/zaa010.130

P002 IBD PARTNERSHIPS: UNDERSTANDING PATIENTS VS. CLINICIANS PERSPECTIVES OF IBD TREATMENT OPTIONS TO IMPROVE SHARED DECISION-MAKING

2020· article· en· W4233120422 on OpenAlexaboutno aff
Sandra Zelinsky, Catherine S. Finlayson

Bibliographic record

VenueInflammatory Bowel Diseases · 2020
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFocus groupConversationPatient participationHealth careQualitative researchParticipatory action researchQuality of life (healthcare)NursingFamily medicinePublic relationsPsychologyBusinessMarketingPolitical science

Abstract

fetched live from OpenAlex

Abstract Background The patient is the only constant in the care journey, the person who experiences both processes and the outcomes of care. There is an international shift towards including patients as equal partners in research. Co-producing research with Inflammatory Bowel Disease (IBD) patients to understand their values, needs and priorities when making treatment decisions will potentially improve shared decision-making between IBD patients and their Healthcare Providers (HCPs). To facilitate this process patients and HCPs must have a common understanding of expected medication benefits, risks and the potential impact on quality of life. The information available to facilitate this conversation must be aligned and reflect the priorities that IBD Patients and Healthcare Providers consider when making treatment decisions. Both parties can then share information and work towards an agreement to what treatment plan should be implemented. Aims To understand what matters most to IBD patients when making treatment decisions by conducting a qualitative patient-led peer to peer study which will inform the development of an IBD patient and HCP survey. Methods IBD patients (≥ 18 years of age) were recruited through the IBD clinic at the University of Calgary and via social media. Focus groups were held in three separate provinces (British Columbia, Alberta and Ontario) in both rural and urban locations. The focus groups were facilitated by a Patient Engagement Researcher to alleviate any potential power dynamics and to create a safe space for IBD patients to share their perspectives. A participatory action research approach was used to encourage co-production with participants throughout the focus groups. The focus groups were audio recorded. Flip charts and sticky notes were used for brainstorming and prioritization exercises. All audio and written data were transcribed. Thematic analysis was used to identify emerging themes and patient priorities. Results A total of 21 participants attended the focus groups from both rural and urban locations. Participant diversity ranged in ethnicity and age. Most of the participants were female (18 females and 3 males) of which 4 were biologic naïve and 17 were biologic exposed. The Top 5 IBD Patient Priorities when making treatment decisions are 1) Risks(more serious/long term) 2) Education(Support/Evidence Based Information/Resources) 3) Side Effects(short term/less serious) 4) Efficacy 5) Impact(Quality of Life/ Lifestyle/Logistics). Conclusions Co-producing research ‘with’ and ‘by’ IBD patients helped to generate priorities that matter most to patients when making treatment decisions. The patient priorities will help in the development of an IBD Patient and HCP survey. The results from the two surveys will be compared to understand patient vs. HCP perspectives.

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)0.025
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.025
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.060
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0140.012
Open science0.0020.013
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0200.001

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 head0.131
GPT teacher head0.411
Teacher spread0.280 · 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 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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