Experience of Care as a Critical Component of Health System Performance Measurement: Recommendations for Moving Forward
Bibliographic record
Abstract
People's experiences can provide critical guidance on how to better meet their quality of life and care needs and deploy resources more appropriately. To maximize the utility of experience data and to advance the current debate, we present four recommendations: (1) measuring experiences outside the healthcare system can provide insight into what needs to change within the healthcare system; (2) focusing on patient experience is necessary but insufficient, (family) caregiver insights and experiences require attention and can provide insight into the needs of the patient; (3) moving from "one time/single sector" measurement of experience to iterative, ongoing measurement across sectors better reflects the true lived experience of patients (especially those with complex care needs) and their caregivers; and (4) embedding measurement within engagement-capable environments that adequately resource patients, caregivers, and providers to work together is required to move from collection to meaningful change. Applying these recommendations requires a longer-term vision, shifting from provider-centred to person-centred models of care, and a deep understanding of the structural, cultural, and normative barriers to measuring care experiences.
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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 | 0.393 | 0.561 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.029 | 0.065 |
| Open science | 0.015 | 0.020 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 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".