The Quest for Value in Canadian Healthcare: The Applied Value in Healthcare Framework
Bibliographic record
Abstract
The exponential rise in healthcare costs in developed nations has sharpened the need for greater "value" in healthcare. Porter's (2010) seminal work is one of the most cited definitions and equation for value-based care. The pursuit of greater value in our healthcare system is of paramount importance, yet translating value-based healthcare (VBHC) into a framework that can be effectively utilized in the Canadian system remains a challenge. To address this challenge, we propose that VBHC can be adapted to fit the Canadian healthcare system through (1) visionary leadership for and conceptualization of VBHC at the federal government level and (2) thoughtful application of VBHC at the provincial government level. Our applied value in healthcare framework serves as a platform from which VBHC initiatives, programs and outcome measures can be systematically organized and executed within provincial healthcare systems. This methodical approach could support both provincial ministries and their health systems in pursuit of VBHC and provide the basis for explicit measurement of VBHC success, thereby helping to address the pressing issue of sustainability of the Canadian healthcare system while optimizing patient-centred outcomes of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.018 | 0.054 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".