Bibliographic record
Abstract
The concept of value-based healthcare has been gaining traction, with several issues of Healthcare Policy discussing the agenda and highlighting pockets of excellence.However, we currently have no shared common goal that would define value-based healthcare.Furthermore, we have major limitations in measuring both the cost and benefit components of the concept of value, irrespective of the definition.It is time to make progress, which will include a recognition of the need to engage the public in a discussion around the values of the Canadian healthcare system and the federal government taking a hands-on role for the accountability of value as an outcome. RésuméLe concept de soins de santé axés sur la valeur gagne du terrain.En effet, plusieurs numéros de Politiques de Santé abordent la question et en soulignent les regroupements d' excellence.Cependant, il n'y a actuellement aucun objectif commun pour définir en quoi consistent des soins de santé axés sur la valeur.De plus, il y a d'importantes limites quant à la mesure des coûts et bénéfices associés au concept de valeur, et ce, indépendamment de la définition retenue.Il est temps de faire des progrès, et cela comprendra la reconnaissance du besoin d' engager le public dans une discussion sur les valeurs du système de santé canadien et sur le rôle du gouvernement fédéral pour tenir compte de la valeur en tant que résultat.
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 | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.027 | 0.039 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.015 | 0.031 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".