Using personas and journey maps as knowledge translation tools to enhance clinicians’ interpretation of PROM scores
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.
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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 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".