Patients’ Experiences and Priorities for Accessing Gastroenterology Care
Bibliographic record
Abstract
BACKGROUND: Wait times for gastroenterology care in Canada exceed recommended benchmarks set by the Canadian Association of Gastroenterology wait-time consensus. Patient-centered prioritization tools may help improve efficiency. METHODS: We conducted a survey on gastroenterology outpatients assessing their experience with accessing care, global health status and health care service utilization while waiting for a gastroenterology appointment. Thematic analysis of survey results informed the questions for a discrete choice experiment (DCE). Three attributes included were the following: clinical indication, functional status and time already waiting, which the study patients considered when prioritizing hypothetical patients. The DCE was analyzed using a conditional logit model. RESULTS: One hundred seventy-three patients completed all questions and were included in the final analysis. Over 80% reported good or excellent physical and mental health with 11% utilizing health care resources while waiting; 14% had waited more than 25 weeks for their appointment. Seventy-seven per cent of the patients were satisfied or better with their experience. Eighty-one per cent of the patients agreed with a prioritization system. Patients would prioritize a patient with a potentially more severe diagnosis or functional impairment over a patient with a less severe diagnosis clinical or functional impairment who had been waiting longer. The most severe clinical attributes were prioritized over the most severe functional attributes. CONCLUSION: Patients support a prioritization tool for access to gastroenterology care. DCE indicated that patients are willing to wait longer in order for those with more severe clinical or functional attributes to be seen earlier. The relative times patients are willing to wait could be used to create a prioritization model for outpatients referred to gastroenterology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".