Educating patients about patient-reported outcomes—are we there yet?
Bibliographic record
Abstract
BACKGROUND: Using Patient Reported Outcome Measures (PROMs) in clinical settings can improve patient outcomes by enhancing communication between patient and provider. There has been significant improvements in the development of PROMs, their implementation in routine patient clinical care, training physicians and other healthcare providers to interpret the PROMs results to identify any issues reported by the patient, and to use the PROMs results to provide or modify the treatment. MAIN BODY: Despite the increased use of PROMs, the lack of PROM completion by patients is a major concern in the optimal use of PROMs. Studies have shown several reasons why patients do not complete PROMs and one of the reasons is their lack of understanding of the significance of PROMs and their utility in their clinical care. While examining the various strategies that can be used to improve the uptake of PROM completion by patients, educating patients about the use of PROMs has been recommended. There is less evidence on how patients are trained or educated about PROMs. It may also be possible that the patient education strategies are not reported in the publications. This brings up the question of evaluation of the educational strategies used. CONCLUSION: Our symposium at the 2023 ISOQOL conference brought together a range of experiences and learning around patient-centered PROMs educational activities used in the Netherlands, Canada, and the UK. This commentary is aimed to describe the lay of the land about educational activities around the use of PROMs in clinical care for patients, recognizing the gaps, and posing questions to be considered by the research and clinical community.
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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 | 0.081 | 0.368 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.028 | 0.045 |
| Insufficient payload (model declined to judge) | 0.006 | 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".