ANALYSING CONTRACTUAL ENVIRONMENTS: LESSONS FROM INDIGENOUS HEALTH IN CANADA, AUSTRALIA AND NEW ZEALAND
Bibliographic record
Abstract
Contracting in health care is a mechanism used by the governments of Canada, Australia and New Zealand to improve the participation of marginalized populations in primary health care and improve responsiveness to local needs. As a result, complex contractual environments have emerged. The literature on contracting in health has tended to focus on the pros and cons of classical versus relational contracts from the funder's perspective. This article proposes an analytical framework to explore the strengths and weaknesses of contractual environments that depend on a number of classical contracts, a single relational contract or a mix of the two. Examples from indigenous contracting environments are used to inform the elaboration of the framework. Results show that contractual environments that rely on a multiplicity of specific contracts are administratively onerous, while constraining opportunities for local responsiveness. Contractual environments dominated by a single relational contract produce a more flexible and administratively streamlined system.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".