The place of private care governance in the <scp>S</scp>outh <scp>A</scp>frican health care system
Bibliographic record
Abstract
BACKGROUND: South Africa essentially has two health care systems-the public and private ones. While much is known about how the public system operates, little work has been conducted on the private sector, perhaps not surprisingly in a profit-oriented, proprietary system. But it is a massive system with its own agenda, interests, and organizations. In this paper, we address the place of private care governance issues, one seen by government as maldistributed, costly, and controlled by few groups and the medical search for profit. METHODS: Using qualitative in-depth interviews, 10 top executive managers of the hospital were asked about its functionality in terms of patient care, profitability, and the practice of governance. Data were analyzed based on themes using NVivo 10 software. RESULTS: The study demonstrates that private hospital functionality finds meaning in board structure, composition and functions, purposeful governance practices as evidenced in well-designed management structures and roles, systematizing governance through the planning of activities, and devising appropriate strategies to deal with both internal and external pressures in the health care environment. CONCLUSION: The study findings establish that shareholders and managers goals converge resulting in the institutionalization and consolidating of relational governance practices in the hospital. Yet other stakeholders appeared to be sidelined.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".