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Record W4391873613 · doi:10.1093/jcag/gwad061.256

A256 NAVIGATING IBD CARE : HEALTHCARE PROVIDERS’ INSIGHTS ON IMPLEMENTATION OF NURSE NAVIGATOR-LED IBD FLARE PATHWAY

2024· article· en· W4391873613 on OpenAlexaffabout
J Reuangrith, Jennifer Jones, Noelle Rohatinsky, Geoffrey C. Nguyen

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversity of TorontoUniversity of SaskatchewanDalhousie University
Fundersnot available
KeywordsHealth careNursingMedicinePolitical science

Abstract

fetched live from OpenAlex

Abstract Background Disparities in access to healthcare for persons living with inflammatory bowel disease (IBD) persist. An IBD nurse navigational role promises to improve access to evidence-based specialist care by minimizing inequities in access to care. Little is known about how to implement a provincial nurse navigational role in the Canadian health system. Aims This study aims to explore healthcare providers' perceptions of barriers and facilitators to the implementation of an evidence-based IBD flare pathway through a provincial nurse navigational role. Methods This was a national qualitative study performed in collaboration with the CCC PACE Program, in which two virtual semi-structured group interviews were conducted in 2022. One group interview was with six IBD nurses from three provinces, and the second was with five gastroenterologists from five provinces. Participants were purposefully recruited. The Consolidated Framework for Implementation Research (CFIR) and the Capability, Opportunity, Motivation, and Behaviour (COM-B) frameworks informed the interview script development. Data were transcribed verbatim and subjected to content analysis, with major themes categorized into the COM-B domains. The results of preliminary data analysis are presented. Results While IBD nurses emphasized the critical role of effective communication tools, standardized referral systems, strong administrative support and patient education and engagement as facilitators, they also highlighted challenges like patient resistance, variability of training and the necessity for tailored patient care. Other barriers identified were the medical complexity of managing overlapping conditions, remote vs in-person considerations and the perception and definition of a flare. On the other hand, IBD-focused gastroenterologists emphasized the potential of integrating pathways into electronic medical records and the value of empowering nurse navigators. However, they pointed out concerns regarding the inherent variability in disease presentation, entrenched medical beliefs, comfort levels with nurse-led pathways and the inherent diversity in medical practices. Both groups identified resource constraints as a barrier and an effective triage process as a facilitator. Conclusions This study sheds light on the multifaceted perceptions of healthcare providers concerning the implementation of an evidence-based IBD flare pathway through a provincial nurse navigational role. These insights underline the complexity of implementing a nurse navigational role and the need for targeted interventions and resources to address the identified barriers while leveraging the facilitators. Funding Agencies CCC

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)0.011
metaresearch head score (Gemma)0.019
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: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.578

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.004
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.304
Teacher spread0.292 · 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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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