Examining Primary Healthcare Performance through a Triple Aim Lens
Bibliographic record
Abstract
PURPOSE: This study sought to apply a Triple Aim framework to the measurement and evaluation of primary healthcare (PHC) team performance. METHODS: Triple Aim components were populated with 10 dimensions derived from survey and health administrative data for 17 Family Health Teams (FHTs) in Ontario, Canada. Bivariate analyses and rankings of sites examined the relationships among dimensions and among Triple Aim components. RESULTS: Readily available measures to fully populate the Triple Aim framework were lacking in FHTs. Within sites, there was little consistency in performance across the Triple Aim components (health, patient experience and cost). CONCLUSIONS: More and better measures are needed that can be readily used to examine the Triple Aim performance in PHC teams. FHTs, in this study, are partially achieving Triple Aim goals; however, there was a lack of consistency in performance. It is essential to collect appropriate measures and attend to performance across all components of the Triple Aim.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads 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".