Bibliographic record
Abstract
McGrail and colleagues (2011) have begun an important discussion to fill a major gap in our understanding of how healthcare – and other factors – affects individuals ’ health status over time. The measurement of health outcomes in Canada has concentrated (unfor-tunately, in retrospect) on the “sharp end ” of the system. As a result, it focuses on impor-tant but crude outcome measures such as hospital readmissions, 30-day and sometimes one-year mortality rates and hospital-acquired pathologies. But the vast majority of every-one’s health and healthcare experience takes place outside hospitals and, as the authors note, acute care measures are quite meaning-less in other settings. AbstrAct McGrail and colleagues have made a strong case for including PROMs into the everyday healthcare system. This commentary focuses on translating their argument into policy and practice. PROMs can no longer be considered a “nice to have”; they are core elements of a patient-centred, quality-oriented healthcare system. Putting PROMs at the centre of the health information agenda signals a major shift in the orientation of the system. Their development should be mandatory, and they should be used to support quality improvement, accountability, and clinical practice rede-sign, particularly in primary care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.856 | 0.789 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".