Bibliographic record
Abstract
This paper explores the feasibility of funding community rehabilitation services based on the outcomes that interventions produce. Community rehabilitation services are most often paid for by the number of client visits or the specific services delivered. Neither of these two methods incent interventions that help to keep patients from accessing more costly upstream acute services such as acute and emergency care. Preventing patients from unnecessarily accessing costly acute services and shortening the length of stay in care are of principal concern to Alberta Health Services (AHS) and the provincial government. As the cost of health services continues to rise, administrators and health policy planners need to look at alternative ways of funding services in order to gain efficiencies and invest in services that prevent a more costly alternative of care. The Social Impact Bond (SIB) is a promising newly piloted type of funding contract that is based on payment for specific outcomes that negate the utilization of more costly care. One of the main benefits of the SIB is that the capital needed for funding services is provided by private investors who have an interest in the population that the program is targeting. However, like any type of funding mechanism that is contingent on outcomes, paying for outcomes in health is challenging due to the complexity of identifying clear performance measures, accessing proper data, and meaningfully measuring change.
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 | 0.030 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.070 | 0.009 |
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".