Public perceptions of key performance indicators of healthcare in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between public perceptions of key performance indicators assessing various aspects of the health-care system. DESIGN: Cross-sequential survey research. Annual telephone surveys of random samples of adult Albertans selected by random digit dialing and stratified according to age, sex and region (n = 4000 for each survey year). The survey questionnaires included single-item measures of key performance indicators to assess public perceptions of availability, accessibility, quality, outcome and satisfaction with healthcare. Cronbach's α and factor analysis were used to assess the relationship between key performance indicators focusing on the health-care system overall and on a recent interaction with the health-care system. SETTING: The province of Alberta, Canada during the years 1996-2004. PARTICIPANTS: Four thousand adults randomly selected each survey year. MAIN OUTCOME MEASURE(S): Survey questions measuring public perceptions of healthcare availability, accessibility, quality, outcome and satisfaction with healthcare. RESULTS: Factor analysis identified two principal components with key performance indicators focusing on the health system overall loading most strongly on the first component and key performance indicators focusing on the most recent health-care encounter loading most strongly on the second component. Assessments of the quality of care most recently received, accessibility of that care and perceived outcome of care tended to be higher than the more general assessments of overall health system quality and accessibility. CONCLUSION: Assessments of specific health-care encounters and more general assessments of the overall health-care system, while related, nevertheless comprise separate dimensions for health-care evaluation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".