Designing a Health System Performance Assessment Model for Iran
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».