Designing a Health System Performance Assessment Model for Iran
Bibliographic record
Abstract
Introduction: Health system performance assessment provides appropriate information about the status of health systems for governments and communities. Therefore, in the recent decade, many countries have focused on performance assessment and reporting in order to develop methods and tools as a mean to help achieving health goals. The present study tries to design an indicator-based model (including general aspects and related indicators) for health system assessment in Iran. Methods: This descriptive comparative applied research was carried out during 2008-2009 and included three phases: reviewing theoretical concepts, preparing health system performance assessment indicators draft and building consensus. Required data was collected via environmental scanning and face to face and web-based interviewing. Environmental scanning did not include a study population and the models extracted through this stage were used as information sources. However, the study population during interview and building consensus phases consisted of 31 Iranian health system experts. The reliability and validity of forms used in interviews were confirmed by the experts and test-retest, respectively. We used a purposive approach and opportunistic sampling method to determine the interviewees. Modified Delphi technique was utilized for building consensus. In order to analyze the data, descriptive statistics (percent, mean and standard deviation) was applied. In the environmental scanning stage of research, performance assessment initiatives were identified in Canada, Australia, New Zealand, the United Kingdom and the United States. In addition, transnational performance assessment frameworks of the World Health Organization (WHO), Organization for Economic Cooperation and Development (OECD), the International Organization for Standardization (ISO), Commonwealth Fund and the United States Agency for International Development (USAID) were reviewed and existing indicators in Iran were collected. In the interviewing stage, indicators proposed by the interviewees were obtained. Finally, all identified indicators were classified in 31 criteria to form the initial draft of indicators. Results: For consensus building, 2 processes of modified Delphi were conducted. In the first process, after 4 rounds, 14 criteria were selected for Iranian health system performance assessment including public health status, governance, accessibility, health expenditure, financing and equity, primary health care, aging care, quality of services, insurance system, hospital performance, research and development, privatization, efficiency and productivity, technology and health information system and also health outcomes. In the second process of Delphi, consensus was obtained on 175 indicators. Conclusion: The designed result- and indicator–based model provides an instrument for reviewing country's health system. Applying this model will offer policy–makers a major opportunity for performance improvement over time. Keywords: Indicators; Performance Assessment; Healthcare Systems; Iran.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".