The Characteristics of Patient Satisfaction Measures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Information on patient satisfaction is considered a way of including patients' perspectives in the planning and assessment of services. The study of patient satisfaction is a relatively new field, and despite the surge in popularity and use of satisfaction measures during the past three decades, different issues remain to be explored. This is not meant to dissuade clinicians from using satisfaction measures, but rather to allow them to proceed in a thoughtful way, recognizing what these measures can reasonably show us about patients' perceptions of the care and treatment interventions they receive. The proposed approach to classifying the characteristics of patient satisfaction measures should help to highlight potential reasons for variation in results when satisfaction measures perform differently and will be of value if it increases the specificity with which clinicians select measures to achieve their purposes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it