Great expectations from the Chair of Evidence-Based Health Care and Knowledge Translation
Notice bibliographique
Résumé
E medicine (EBM) has been introduced to the Kingdom of Saudi Arabia (KSA) over a decade ago and the first workshop for critical appraisal training was organized in 1997 by the Family Medicine Department of King Saud University (personal communication). Following that pioneering workshop the ideology of EBM was embraced religiously by many academic and service institutions with the development of many active groups, including Jeddah group for EBM, Madina group, and others. This EBM activity is noticeable in other Arab countries as well, such as Egypt, Syria, Bahrain, Sudan, Jordan, Sultanate of Oman, and the United Arab Emirates (UAE). The development of these groups was followed by the development of collaborative groups such as the National and Gulf Centre for EBM, and the Arab Federation for EBM. All these groups have been very active in spreading EBM by organizing workshops for doctors and other allied medical staff in the form of foundation knowledge of EBM and training of the trainers courses and workshops. More recently, 13 countries from the East Mediterranean region Joined the Evidence Informed Policy Network (EVIPnet), one of the WHO organizations that work towards establishing an evidence-informed health policy in participating countries.1 A step forward was taken by the Ministry of Health in the Kingdom of Bahrain by establishing a branch of the United Kingdom Cochrane Center in 2005 in Bahrain.2 The main objectives of the center are to provide training for authors of systematic reviews and to work as a communication link between authors and different Cochrane groups, in addition to its role in translating Arabic medical literature.2 Some individual efforts paid dividend, and during the last few years, we have noticed an increasing number of Cochrane authors from the Arab World including Egypt, Bahrain, Sudan, KSA, UAE, and Syria.2 The main goal behind EBM or evidence based healthcare (EBHC) is to improve the quality of healthcare, and to standardize an effective care for patients according to the best available evidence. Then, the concept was generalized from individual health provider or individual setting, to include evidencebased health policy (EBHP) to indicate the adoption of the legislative and the statutory organizations, such as the Ministry of Health, to EBHC, and to base its decision of fund allocation, among other considerations, on evidence for the most effective and cost effective medication, and health technology. More recently, the concept of knowledge translation (KT) was introduced, to indicate the process by which evidence is communicated from researchers to the end users including clinicians, patients and policy makers. The Canadian Institute for Health Research defines KT as “the exchange, synthesis and ethically-sound application of research findings within a complex set of interactions among researchers and knowledge users. In other words, knowledge translation can be seen as an acceleration of the knowledge cycle; an acceleration of the natural transformation of knowledge into use.”3 However, many difficulties face this adoption of EBHP all over the world, including the prospect by which health problem is considered a priority, the different languages that the scientists and the politicians speak, and the time frame in which each group operates.4 To overcome these difficulties, bridges of communication should be established between end users including policy makers, and the evidence generators to facilitate KT.5 These bridges are missing from the Arab World as much as the
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 | 0,005 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».