Let's All Go to the PROM: The Case for Routine Patient-Reported Outcome Measurement in Canadian Healthcare
Bibliographic record
Abstract
Overall life expectancy in Canada is among the highest in the world and research evidence suggests that the healthcare system is part of the reason for this. However, patient waits, low international rankings and continued expenditure growth all provide a buttress against complacency. There can be little doubt that improvement can and must happen. Improvement depends on information, and more specifically information about outcomes of care. Without sound analysis of what works in the real world when applied to real patients, we have not done our jobs as stewards of the healthcare system. Current outcomes information in Canada is limited and tends to focus on measures of failure (e.g., hospital readmissions) rather than measures of success (e.g., improvement in functioning). Patient reported outcome measures (PROMs) must become part of regular data collection in the healthcare system. The importance of this is even more pronounced given that healthcare is now dominated by chronic conditions that need to be managed over long periods of time. We offer three recommendations for action: that we begin immediately to collect PROMs in elective surgery; that we start small-scale and coordinated experiments on the implementation of PROMs in care for chronic conditions; and that we convene a pan-Canadian working group to help coordinate and organize these activities. We recognize the challenges these issues raise, but our contention is that there are even greater challenges in continuing on as we are.
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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 | 0.313 | 0.459 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.016 | 0.010 |
| Research integrity | 0.009 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 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".