Psychometric refinement of an outpatient, visit‐specific satisfaction with doctor questionnaire
Bibliographic record
Abstract
Measuring patient's satisfaction with their physician is gaining interest but requires a questionnaire that is valid, reliable and acceptable to patients. We previously published a self-administered visit-specific satisfaction with physician questionnaire for cancer patients. Eighty outpatients at a Canadian Cancer Center completed the Princess Margaret Hospital Patient Satisfaction with Doctor Questionnaire and the FACT-G questionnaires along with demographic information just after clinic visit and again 3-5 days later. Exploratory factor analysis extracted two factors, labeled 'physician disengagement' and 'perceived support,' with average coefficient alpha values of 0.93 and 0.90. Test-retest reliability was 0.83 and 0.73, respectively, for the two factors. Confirmatory factor analysis applied to the data from 174 patients in the original study indicated excellent goodness of fit. PMH/PSQ-MD correlated moderately with FACT-G (average r=0.37, p<0.005). The PMH/PSQ-MD questionnaire is a brief, valid and reliable questionnaire that taps two complementary facets of patient satisfaction.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.000 |
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".