Great expectations from the Chair of Evidence-Based Health Care and Knowledge Translation
Bibliographic record
Abstract
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
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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 | 0.264 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.033 | 0.073 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".