Development of a Patient-Reported Experience Measure Tool for Ambulatory Patients With Acute Unexpected Needs: The APEX Questionnaire
Bibliographic record
Abstract
Background: The aim of this study was to develop a patient-reported experience measure (PREM) for comparing the experience of care received by ambulatory patients with acute unexpected needs presenting in emergency departments (EDs), walk-in clinics, and primary care practices. Methods: The Ambulatory Patient EXperience (APEX) questionnaire was developed using a 5-phase mixed-methods approach. The questionnaire was pretested by asking potential users to rate its clarity, usefulness, redundancy, content and face validities, and discrimination on a 9-point scale (1 = strongly disagree to 9 = strongly agree). The pre-final version was then tested in a pilot study. Results: The final questionnaire is composed of 61 questions divided into 7 sections. In the pretest (n = 25), median responses were 8 and above for all dimensions assessed. In the pilot study, 63 participants were enrolled. Adjusted results show that access, cleanliness, and feeling treated with respect and dignity by nurses and physicians were significantly better in the clinics than in the ED. Conclusion: We developed a questionnaire to assess and compare experience of ambulatory care in different clinical settings.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".