Measuring value in healthcare from a patients’ perspective
Bibliographic record
Abstract
Throughout the world there is a growing recognition that the patient’s perspective is highly relevant to efforts to deliver high-value patient-centered care and to improve the quality and effectiveness of healthcare. One of the key challenges to achieving this is the limited measurement of outcomes that matter most to patients. At an OECD conference in 2017, Ministers of Health from around the world stated, “We need to invest in measures that will help us assess whether our health systems deliver what matters most to people” [ 1 ]. The introduction of patient-reported outcome measures (PROMs)—measurement instruments designed to assess the status of a patient’s health condition that comes directly from the patient [ 2 ]—is one strategy to ensure that patient’s perspectives are systematically incorporated into the approaches of delivering healthcare services, and valuing the performance of the healthcare system [ 3 , 4 , 5 , 6 ]. PROMs could act to improve the quality of care in the same way as any other benchmarking tool [ 7 ], and some suggest that PROMs have the potential to transform healthcare [ 3 ].
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 | 0.024 | 0.117 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.027 | 0.039 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".