Measuring Patient Experiences: Is It Meaningful and Actionable?
Bibliographic record
Abstract
Performance measurement must be meaningful to those being asked to contribute data and to the clinicians who are collecting the information. It must be actionable if performance measurement and reporting is to influence health system transformation. To date, measuring patient experiences in all parts of the healthcare system in Canada lags behind other countries. More attention needs to be paid to capturing patients with complex intersecting health and social problems that result from inequitable distribution of wealth and/or underlying structural inequities related to systemic issues such as racism and discrimination, colonialism and patriarchy. Efforts to better capture the experiences of patients who do not regularly access care and who speak English or French as a second language are also needed. Before investing heavily into collecting patient experience data as part of a performance measurement system the following ought to be considered: (1) ensuring value for and buy-in from clinicians who are being asked to collect the data and/or act on the results; (2) investment in the infrastructure to administer iterative, cost-effective patient/family experience data collection, analysis and reporting (e.g., automated software tools) and (3) incorporating practice support (e.g., facilitation) and health system opportunities to integrate the findings from patient experience surveys into policy and practice. Investment into the infrastructure of measuring, reporting and engaging clinicians in improving practice is needed for patient/caregiver experiences to be acted upon.
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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 | 0.254 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".