Unlocking the “Black Box” of Hospital Competition: Multiple Case Studies in Government-Funded and Regulated Health Systems
Bibliographic record
Abstract
This dissertation explores the phenomenon of competition in healthcare, focusing on highly regulated and publicly-funded settings, specifically two Canadian provinces with very different system structures. Empirical approaches included multiple case studies, interviews, and thematic-analysis. Despite the absence of a profit motive and the threat of market exclusion, competition is present in both systems, even across hospital sites merged into a single provincial authority. Similarities in the manifestation of competition across the two provinces may result from common organizational features and system pressures, including advocacy by hospital/site leaders, scarcity, technological innovations, uncertainty, and stakeholder expectations. However, in line with earlier studies, demand-side factors do not drive competition. Moreover, hospital leaders and referring physicians found information on the quality of care difficult to understand and relied on different strategies to assess care quality. Instead, factors, such as the potential for embarrassment, drove competition among hospitals/sites, underscoring the importance of reputation as a critical element in stimulating and defining competition among hospitals. Competition is also driven by hospital leaders’ intrinsic motivation to improve care quality. These findings align with existing literature and contribute to the literature by revealing clearer conceptualizations and pathways pertinent to healthcare competition. The findings reported in this dissertation also articulate key elements in the regulatory environment, namely health system typology, governance, and policy instruments, the combination of which shapes competition. They show that despite similarities between the systems' competition dynamics, the differences in these system traits create specific forces in each system and create differences in how competition manifests. The dissertation findings also signify the need for organizational legitimacy and pressure to conform to defining competition, further resonating with existing public-sector literature and institutional theories of organization. Despite the highly regulated nature of the Canadian health system, competition is present among hospitals, even when they are part of a single provincial corporation. Furthermore, despite important differences in the regulatory context, there are important similarities in how competition manifests and is perceived in the two provinces studied. The drivers of this competition hold important implications for how health policies can stimulate positive competition and avoid destructive types of competition.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".