What the policy and stewardship landscape of a national health research system looks like in a developing country like Iran: a qualitative study
Notice bibliographique
Résumé
Abstract Background The health research system (HRS) is an important national priority that requires a systematic and functional approach. Evaluating the HRS of Iran as a developing country and identifying its challenges reveals the stewardship-related role in how the whole system is operating well. This study aims to assess the HRS in terms of stewardship functions and highlight the enhancement points. Methods This study was carried out between March 2020 and April 2021 using a systematic review and meta-synthesis of evidence to examine the Iranian HRS stewardship challenges and interview 32 stakeholders, using a critical case sampling and snowballing approach which included both semi-structured and in-depth interviews. The interviewees were selected based on criteria covering policy-makers, managers, research bodies and nongovernmental organizations (NGOs) in health research-related fields like higher education, research, technology, innovation and science. All data were analysed using content analysis to determine eight main groups of findings under three levels: macro, meso, and micro. Results Analysis of the findings identified eight main themes. The most critical challenges were the lack of an integrated leadership model and a shared vision among different HRS stakeholders. Their scope and activities were often contradictory, and their role was not clarified in a predetermined big picture. The other challenges were legislation, priority-setting, monitoring and evaluation, networking, and using evidence as a decision support base. Conclusions Stewardship functions are not appropriately performed and are considered the root causes of many other HRS challenges in Iran. Formulating a clear shared vision and a work scope for HRS actors is critical, along with integrating all efforts towards a unified strategy that assists in addressing many challenges of HRS, including developing strategic plans and future-oriented and systematic research, and evaluating performance. Policy-makers and senior managers need to embrace and use evidence, and effective networking and communication mechanisms among stakeholders need to be enhanced. An effective HRS can be achieved by redesigning the processes, regulations and rules to promote transparency and accountability within a well-organized and systematic framework.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 tête enseignante, 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 ».