Development and validation of the Alimetry Gut-Brain Wellbeing Survey: a novel patient-reported mental health scale for patients with chronic gastroduodenal symptoms
Bibliographic record
Abstract
Objective There is currently a lack of validated questionnaires designed specifically to assess mental health within patients with chronic gastroduodenal symptoms. This research describes the multi-phase process used to develop and validate a novel mental health scale for patients with chronic gastroduodenal symptoms, the Alimetry® Gut-Brain Wellbeing (AGBW) Survey. Methods A patient-centered multi-phase process was implemented. In Phase 1, the most relevant concepts for this patient population were selected from existing mental health scales, using data from 79 patients. In Phase 2, an interdisciplinary panel of experts generated scale items. In Phase 3, the scale underwent pre-testing with gastroenterologists ( n = 9), health psychologists ( n = 3), and patients ( n = 12), with feedback incorporated over multiple rounds. Lastly, the psychometric properties of the scale were assessed in a sample of 311 patients via an online survey. Results The AGBW Survey comprises a patient preface, 10 close-ended questions, and an optional open-ended question. This multidimensional scale assesses general mental health, alongside specific subscales relating to depression, stress, and anxiety. The subscale and total scores demonstrated high internal consistency ( α = 0.91 for the total scale; α = 0.72–0.86 for subscales) and good convergent, divergent, concurrent validity, and known groups validity, with large effect sizes. Conclusion The AGBW Survey is a brief, valid, and reliable scale for assessing mental health in patients with chronic gastroduodenal symptoms. It can be used as a tool to complement physiological tests and has the potential to guide psychological referrals, inform multidisciplinary management, and evaluate treatment outcomes.
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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 | 0.006 | 0.010 |
| 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 | 0.000 | 0.002 |
| 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".