How to Improve Interpretability of Patient-Reported Outcome Measures for Clinical Use: A Perspective on Measuring Abilities and Feelings
Bibliographic record
Abstract
Jacek A Kopec1,2 1School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada; 2Arthritis Research Canada, Vancouver, BC, CanadaCorrespondence: Jacek A Kopec, School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada, Email jkopec@arthritisresearch.caAbstract: Two general classes of concepts measured by patient-reported outcome measures (PROMs) are abilities and feelings. Over the past several decades, there has been a significant progress in measuring both. Nevertheless, current multi-item scales are subject to criticism related to scale length, score dimensionality, interpretability, cultural bias, and insufficient detail in measuring specific domains. To address some of these issues, the author offers an alternative perspective on how questions about abilities and feelings could be formulated. Abilities can be defined in terms of a relationship between the level of performance and the associated perception of difficulty, and represented graphically by an ability curve. For feelings, it may be useful to measure frequency and intensity jointly to determine the proportion of time in each level of intensity. The resultant frequency × intensity matrix can be presented as a bar graph. Empirical data to support the feasibility and validity of these approaches to PROM design are provided, potential advantages and limitations are discussed, and some future research avenues are suggested.Keywords: patient-reported outcome measures, PROMs, abilities, feelings, measurement, quality of life
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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 | 0.613 | 0.795 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".