Public assessment of key performance indicators of healthcare in a Canadian province: the effect of age and chronic health problems
Bibliographic record
Abstract
This study explores the effect of age and chronic conditions on public perceptions of the health system, as measured by the Key Performance Indicators (KPIs) of healthcare, in the province of Alberta in Canada. Drawing from data collected by Government of Alberta's Department of Health and Wellness, this research examines two key questions: (1) Do people in the 65+ age group rate the KPIs of healthcare (i.e., availability, accessibility, quality, outcome, and satisfaction) more favorably compared to people in younger age groups in Alberta? (2) Does the rating of KPIs of healthcare in Alberta vary with different chronic conditions (i.e., no chronic problem, chronic illnesses without pain, and chronic pain)? The findings indicate that people in the older age group tend to rate the KPIs of healthcare more favorably compared to younger age groups in Alberta, net of socio-demographic factors, self-reported health status, and knowledge and utilization of health services. However, people experiencing chronic pain are less likely to rate the KPIs of healthcare favorably compared to people with no chronic health problem in Alberta. Discussion includes implications of the findings for the healthcare system in the province.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".