Using personas and journey maps as knowledge translation tools to enhance clinicians’ interpretation of PROM scores
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Patient-reported outcome measures (PROMs) are common tools for assessing patients’ health, disease condition, functional status, well-being, and quality of life that can achieve person-centred care. While PROMs provide valuable numeric scores, they do not capture contextual depth, thereby making it difficult for clinicians to interpret scores in ways that reflect the complexities of individual lived experiences. This commentary introduces personas and journey maps as educational knowledge translation tools to support a more holistic interpretation of PROM data. Personas integrate PROM data with patient narratives to create relatable archetypes that reflect the values, challenges, and priorities of various patient groups. Journey maps, in turn, visually trace patients’ interactions with the healthcare system over time, identifying key events and transitions that influence their experiences. Together, these tools offer clinicians a story-informed framework to interpret PROM data in ways that are grounded in patient experience. Integrating PROM data within personal and temporal contexts can enhance the relevance, empathy, and practical utility of PROMs for person-centred care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it