Primary healthcare provider-perceived barriers to implementing an evidence-based pathway for undifferentiated lower gastrointestinal tract symptoms: A qualitative inquiry
Bibliographic record
Abstract
BACKGROUND: Primary healthcare providers play a critical role in diagnosing and managing digestive disorders. Standardized clinical care guidelines have been developed, but with limited and inconsistent implementation. An evidence-based gastroenterology clinical care pathway (GUTLINK) has been proposed in one region of Canada; however, little is known in the medical literature about potential barriers to pathway implementation within primary care. We aimed to identify behavioral and environmental barriers and facilitators to implementation of evidence-based care pathways for undifferentiated lower gastrointestinal tract symptoms in primary care. METHODS: One-on-one semi-structured interviews were conducted with primary healthcare providers between September 2021 and May 2022. Interview script development was guided by the COM-B framework. Interviews were transcribed and data were analyzed using an inductive thematic analysis approach. RESULTS: A total of 15 primary healthcare provider interviews were conducted. Several key barriers to GUTLINK implementation were identified in all three domains of the COM-B framework. Key barriers included Capability (e.g., Physician Knowledge and Access to Allied Health), Opportunity (e.g., Access to diagnostic tools), and Motivation (e.g., Comfort with managing cases and optimism). Some of these barriers have not previously been identified in medical literature. CONCLUSIONS: Evidence-based clinical care pathways have the potential to support access to quality gastroenterology care, yet primary healthcare providers in this study identified several barriers to implementation. Potential solutions exist at the individual and clinic levels (e.g., greater education, improved provider-specialist communication), but must be supported with systems-level changes (e.g., increased funding for gastrointestinal care and e-Health platforms) to support pathway implementation and improve quality of care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".