Assessing the Educational Needs of Canadian Gastroenterologists and Gastroenterology Nurses: Challenges to Optimal Care in Crohn’s Disease
Bibliographic record
Abstract
OBJECTIVE: A national needs assessment of Canadian gastroenterologists and gastroenterology nurses was undertaken to determine the perceived and unperceived educational and performance barriers to caring for patients with Crohn's disease (CD). METHODS: A triangulated, mixed-method approach (qualitative and quantitative) was used to determine the nature and extent of knowledge gaps and barriers in the care of patients with CD. RESULTS: Qualitative interviews were conducted with nine gastroenterologists, four gastroenterology nurses and nine patients with CD. Based on this exploratory research, a survey was designed and launched nationally (37 gastroenterologists, 36 gastroenterology nurses). Findings indicated that Canadian gastroenterologists and gastroenterology nurses lacked clarity regarding their roles and responsibilities across the continuum of CD care, and face communication gaps within the health care team, undermining their effectiveness. Gastroenterologists identified challenges in optimal diagnosis due to unclear testing and diagnostic criteria. They recognized knowledge gaps when treating patient subgroups and in prescribing biological therapies. Furthermore, gastroenterologists self-identified gaps in skill, knowledge, and confidence in monitoring disease progression and effectively assessing response to therapy. When managing patients with CD, gastroenterologists expressed challenges with patient issues outside their domain of medical expertise, particularly with the skills needed to facilitate effective patient communication and education that would enhance adherence to recommended treatments. CONCLUSIONS: Educational initiatives should address diagnostic and treatment guidelines, as well as enhancement of clinical performance gaps in health care team processes and the patient-professional therapeutic relationship. To impact care and patient outcomes, these initiatives must be relevant to clinical practice settings and applicable to the practice context.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".