Managing under Managed Community Care: The Experiences of Clients, Providers and Managers in Ontario's Competitive Home Care Sector
Bibliographic record
Abstract
In 1996, a newly elected government in the Province of Ontario, Canada, introduced a managed competition environment into the home care sector through the establishment of a competitive contracting process for home care services. Through 65 in-depth, semi-structured interviews conducted between November 1999 and January 2001, we trace the implementation of this competition contracting policy within Ontario's newly established managed community care environment and assess the effects of competitive contracting against two sets of goals: 1) quality of care goals that consider continuity of care of paramount importance in the provision of home care; and 2) the managed competition goal of increased efficiency. In assessing the implementation of this policy against these goals, we highlight the conflicts that can arise in pursuing different policy goals in response to different formulations of the policy problem that underpin them. We map stakeholder experiences with the competitive contracting policy onto relevant contracting and managed competition literatures. When measured against the goals of quality of care and efficiency, the findings presented here offer a mixed review of the experiences to date with the competitive contracting process introduced in Ontario's home care sector and suggest improvements for managing future competitive contracting processes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.019 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".