Contestability in human services and its impact on service providers: A case study of community aged care in New South Wales
Bibliographic record
Abstract
A major reason for the substantial marketisation of human services over the last quarter century has been a belief that greater contestability has a positive impact on service providers, by enabling new and innovative providers to enter and giving all incumbents more incentive to improve services. Critics dispute this, claiming that the effects are often negative, allowing the entry of profit-maximisers and generating incentives for all providers to cut costs at the expense of good services. This thesis examines these conflicting claims, both theoretically in relation to human services in general, and through an empirical study of one service in one location. The first part of the thesis considers the theoretical implications for the concept of contestability when the product is a human service, and the resulting effects on the structure and operatipn of markets, the type of providers, and the incentives facing providers. A key aim is to assess marketisation on its own terms as far as possible and examine human services industries in the same way as other industries. An analytical framework grounded in conventional economic theory, but adapted to the reality of human services, is developed to examine con testability in specific markets. This framework is then used for the empirical study of the community aged care industry in New South Wales (NSW). This was a mixed-method study, with data obtained primarily from interviews, document analysis, and analysis of funding data. Both parts of the research show that there is substantial, intrinsic market failure in human services. This creates much scope for poor practice by providers, and requires limits on contestability to maximise the outcomes of services. Notwithstanding this, if implemented in a limited and strategic way, with a small number of providers competing on the basis of quality, then contestability can have positive effects on the capacity of providers to deliver good services. Even though there are tight limits on contestability in the NSW community aged care industry, there has been continuing new entry, vigorous competition, stability in the supply of services, and strong incentives for providers to improve the quality, responsiveness and efficiency of their services.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".