Can Routine Collection of Patient Reported Outcome Data Actually Improve Person-Centered Health?
Bibliographic record
Abstract
McGrail et al. have provided an important overview of an argument for the routine collection of patient-reported outcome measures (PROM) data as a critical step toward the improvement of population health in the Canadian healthcare system. In this commentary, the authors argue that equal attention must be paid to knowledge translation in the implementation of routine collection of PROM data to ensure a high quality-clinical response if population health is to be improved. They also argue that, based on their experience in cancer, the complexity of the implementation of PROM data, particularly in chronic diseases, cannot be underestimated. Finally, the authors emphasize the need for standardization in the selection of core PROMs data for routine collection that builds on global efforts to advance the person-centredness of healthcare services and reflects the broad physical, emotional and social domains of health that will be important to capture in chronic disease.
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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 | 0.046 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.052 | 0.051 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".