“One more tool in the tool belt”: A qualitative interview study investigating patient and clinician opinions on the integration of psychometrics into routine testing for disorders of gut-brain interaction
Bibliographic record
Abstract
Abstract Background Psychological comorbidities are common in patients with disorders of gut-brain interaction (DGBIs) and are often linked with poorer patient outcomes. Likewise, extensive research has shown a bidirectional association between psychological factors and gastrointestinal symptoms, termed the gut-brain axis. Consequently, assessing and managing mental wellbeing, in an integrated care pathway, may lead to improvements in symptoms and quality of life for some patients. This study aimed to explore patients’ and gastroenterology clinicians’ opinions on integrating psychometrics into routine DGBI testing. Methods Semi-structured interviews were conducted with 16 patients with a gastroduodenal DGBI and 19 clinicians who see and treat these patients. Interviews were transcribed verbatim and analysed using inductive, reflexive thematic analysis. Results Three key clinician themes were developed: (1) psychology as part of holistic care, emphasising the importance of a multidisciplinary approach; (2) the value of psychometrics in clinical practice, highlighting their potential for screening and expanding management plans; and (3) navigating barriers to utilising psychometrics, addressing the need for standardisation and external handling to maintain the therapeutic relationship. Four key patient themes were also developed: (1) the utility of psychometrics in clinical care, reflecting the perceived benefits; (2) openness to psychological management, indicating patients’ willingness to explore psychological treatment options; (3) concerns with psychological integration, addressing potential stigma and fear of labelling; and (4) the significance of clinician factors, emphasising the importance of clinician bedside manner, knowledge, and collaboration. Conclusions The themes generated from the interviews indicated that patients and clinicians see value in integrating psychometrics into routine DGBI testing. Despite potential barriers, psychometrics would advance the understanding of a patient’s condition and facilitate holistic and multidisciplinary management. Recommendations for navigating challenges were provided, and considering these, patients and clinicians supported the use of psychometrics as mental health screening tools for patients with gastroduodenal DGBIs.
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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 | 0.049 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".