Structuring Horizontal Accountability in Ontario’s Health Care System
Bibliographic record
Abstract
Background: In health systems comprised of independent health care organizations, the structuring of integrated delivery systems relies on organizations structuring horizontal accountability (HA) with one another. Despite this requirement, we know very little about how this is achieved. While scholars acknowledge that HA should exist as complementary to an organization’s vertical accountabilities, research has found that the structuring of HA can be disrupted by strong vertical accountabilities. This research seeks to understand: how can HA be structured within a system historically reliant on vertical accountabilities?Methods: This was an embedded, multi-case, case study research design, utilizing two Ontario-based cases. I utilized multiple qualitative methods with semi-structured interviews with administrators and governors as a primary source of data collection. Drawing on definitions of accountability in the public administration literature and Bovens (2010) and Dubnick and Frederickson’s (2010) concepts of accountability as a mechanism and as a virtue, I developed a preliminary conceptual framework that would guide this research. The focus was exploratory with goals of analytic applicability, generalization, and the expansion of theory. Results: Where organizations are compelled to structure HA, they will structure HA with contingencies that maintain their independence. For HA to be successful, it must be structured in a way that maintains its virtues while also institutionalizing aspects of HA that codify the interdependence of the partners. Actions and behaviours from the principal and governors, and participant willingness to tackle various tensions resulting from the historical environment, will all be core to establishing a facilitative environment for HA. Democratic, political, clinical, and community dimensions of accountability are well suited to HA whereas high degrees of funding, performance, and bureaucratic forms of accountability may determine whether HA will be replaced by a hierarchical scenario. Conclusion: This research adds to the literature by considering necessary processes, mechanisms, and environments for structuring HA and presents conceptual and analytic frameworks that can be utilized by administrators, policy makers, and researchers. This comprehensive approach is important as the presence of mechanisms alone have proven to be insufficient to the successful structuring of HA relationships (Addicott & Shortell, 2014; Andersson & Wikström, 2014).
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".