Introduction of safety and quality standards for private health care providers: a case-study from the Republic of Srpska, Bosnia and Herzegovina
Bibliographic record
Abstract
BACKGROUND: Regulation of private health care providers (PHPs) in middle-income countries can be challenging. Mandatory safety and quality standards for PHPs have been in place in the Republic of Srpska since 2012, but not all PHPs have adopted them yet. Adoption rates have differed among different types of providers. We studied three predominant types of PHPs to determine why the rate of adoption of the standards varies among them. METHODS: This study used a mixed methods approach, which allowed the integration of both quantitative and qualitative data, to develop an explanatory case study. The case study covered three types of private PHPs: pharmacies, dental practices and specialist practices. Primary data were collected through face-to-face semi-structured in-depth interviews and a self-administered postal survey of private health care providers. Our study's theoretical framework was based on the diffusion of innovation theory. RESULTS: The rate of adoption of mandatory standards varied among different types of PHP mainly due to four factors: (1) level of concern about negative financial consequences, such as the risk of fines or of losing contracts with the Health Insurance Fund of the Republic of Srpska; (2) availability of information on the standards and implementation process; (3) level of the relevant professional association's support for the introduction of standards; and (4) provider's perceptions of the relevant health chamber's attitude toward the standards. Opinions conveyed to PHPs by peers slightly negatively influenced adoption of the standards at the attitude-forming stage. Perceived gains in professional status did not have a major influence on the decision to adopt standards. All three types of PHPs perceived the same disadvantages of the introduction of safety and quality standards: associated expense, increased administrative burden and disruption of service provision. CONCLUSIONS: When introducing mandatory quality and safety standards for PHPs, national health authorities need to: ensure adequate availability of information on the relative advantages of adhering to standards; support the introduction of standards with relevant incentives and penalties; and work in partnership with relevant professional associations and health chambers to get their buy-in for regulation of quality and safety of health 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".