Patient-driven research priorities for patient-centered measurement
Bibliographic record
Abstract
BACKGROUND: Patient-centred measurement (PCM) emphasizes a holistic approach wherein the voices of patients are reflected in the standardized use of patient-reported outcome and experience measures and are represented throughout the continuum of measurement activities. Given the challenges of routinely integrating patient self-reports into clinical care decisions, the perspectives of all healthcare system stakeholders, especially patients, is necessary to advance the science of PCM. The purpose of the analysis we report on here was to identify patient-driven research priorities for advancing the science of PCM. METHODS: We analyzed data from seven focus groups that were conducted across British Columbia, Canada and that included a total of 73 patients, using qualitative inductive analysis and constant comparative methods. RESULTS: We found that the patients conveyed a desire for PCM to contribute to healthcare decisions, specifically that their individual healthcare needs and related priorities as they see them are always front and centre, guiding all healthcare interactions. The patients' commentaries highlighted intersecting priorities for research on advancing the science of PCM that would help transform care by (1) enhancing the patient-provider relationship, (2) giving voice to patients' stories, (3) addressing inclusivity, (4) ensuring psychological safety, (5) improving healthcare services and systems to better meet patient needs, and (6) bolstering healthcare system accountability. CONCLUSIONS: These priorities provide direction for future research efforts that would be positioned to make progress towards better health, better care, and better use of resources for individuals and for society.
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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 | 0.581 | 0.505 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.014 | 0.050 |
| Scholarly communication | 0.029 | 0.033 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.012 | 0.023 |
| 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; 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".