Creation of Text Vignettes Based on Patient-Reported Data to Facilitate a Better Understanding of the Patient Perspective: Design Study
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome (PRO) data refer to information systematically reported by patients, or on behalf of patients, without the influence of health care professionals. It is a focal point of the health care system's ambition toward becoming more involving and personalized. It is recognized that PROs provide valuable data. However, despite this recognition, there are challenges related to both patients' and clinicians' accurate interpretations of the quantitative data. To overcome these challenges, this study explores text vignettes as a representation of PROs. OBJECTIVE: This study aimed to develop data-informed text vignettes based on data from the Readiness and Enablement Index for Health Technology (READHY) instrument as another way of representing PRO data and to examine how these are perceived as understandable and relevant for both patients and clinicians. METHODS: The text vignettes were created from participant responses to the READHY instrument, which encompasses health literacy, health education, and eHealth literacy. The text vignettes were created from 13 individual text strings, each corresponding to a scale in the READHY instrument. This study consisted of 3 sequential parts. In part 1, individuals with chronic obstructive pulmonary disease completed the READHY instrument, providing data to be used to create vignettes based on cluster profiles from the READHY instrument. Part 2 focused on the development of scale-based strings representing all READHY dimensions, which were evaluated through iterative cognitive interviews. In part 3, clinicians and patients assessed the understanding and relevance of the text vignettes. RESULTS: Clinicians and patients both understood and related to the text vignettes. Patients viewed the text vignettes as an accurate reflection of their PRO responses, and clinicians perceived the text vignettes as aligned with their understanding of patients' experiences. CONCLUSIONS: Text vignettes can be developed using PRO instruments, with individual scales as input strings. This provides an opportunity to present numeric values in a text format that is understandable and recognizable to most patients and clinicians. Challenges with the vignette's language and layout require customization and clinician training to ensure meaningful interpretation. Findings also support the need to expand the study and enhance clinical relevance with alternative or contextually relevant text vignettes.
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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 | 0.039 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".