Advancing measurement-based care through triangle of care: Development and feasibility of the Transdiagnostic Global Impression – Psychopathology scale for patients and informants
Bibliographic record
Abstract
BACKGROUND: Measurement-based care (MBC) is widely recommended in psychiatry but remains underutilized in routine clinical settings. The Transdiagnostic Global Impression - Psychopathology (TGI-P) scale was developed to provide a brief yet comprehensive assessment of 10 core transdiagnostic symptom domains. To support more inclusive care and promote patient and caregiver engagement in treatment planning, two new versions of the TGI-P, that is, a patient-rated and a separate informant-rated, were developed, complementing the previously published clinician-rated version. METHODS: The patient and informant versions mirror the original clinician-rated TGI-P, assessing the identical 10 domains using a seven-point Likert severity scale, with results displayed via a personalized symptom map. A user satisfaction/feasibility study was conducted with 50 participants (25 patients and 25 caregivers) from the UK and US. After completing the scale, participants provided feedback on its clarity, usability, emotional impact, and comparative utility. RESULTS: Most participants completed the scale in less than 5 min. Instructions were considered clear, and the format was rated easy to follow. Response options were deemed appropriate by 86% of participants, and the visual output was widely appreciated. While one-third reported mild emotional triggering, overall burden was described as manageable. Approximately, three-quarters of participants rated the TGI-P as equal to or better than other tools they had used. CONCLUSIONS: TGI-P patient and informant versions were developed and, informed by the feasibility study, refined to offer brief, user-friendly tools that support multi-informant assessment as input to MBC. Both versions of the TGI-P, with their graphical output, may support shared understanding and collaborative decision making among clinicians, patients, and caregivers. A validation study of the TGI-P is underway.
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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 | 0.023 | 0.042 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".