The First Asynchronous Online Evidence-Based Medicine Course for Syrian Health Workforce: Effectiveness and Feasibility Pilot Study
Bibliographic record
Abstract
BACKGROUND: Evidence-based medicine (EBM) is critical for providing the best scientifically proven patient health care, and it is implemented worldwide in order to improve the quality of the delivered health care. However, not all Syrian health professionals are knowledgeable about the importance, methodology, and implementation of EBM. Providing web-based learning courses on EBM might be effective in improving the EBM knowledge of health care professionals. OBJECTIVE: This study was performed to test the effectiveness and the feasibility of an asynchronous web-based course on EBM in improving the competencies of Syrian health care professionals in terms of EBM. METHODS: A web-based course on EBM was developed in Arabic and uploaded onto the Syrian Virtual University platform. An electronic registration form was designed and distributed to medical groups on social media for registration to this web-based course. Both the pretest and posttest had the same 3 sections to measure the impact of this web-based EBM program on the knowledge, skills, and attitudes of the Syrian health care professionals. The posttest had an additional section for measuring the efficacy and ease of use of this program. Student paired 1-tailed t test was used to analyze the differences in the different assessment sections among the participants. RESULTS: Nineteen participants filled the electronic registration form, but 8 participants did not meet the inclusion criteria. Therefore, the pretest was sent to the remaining 11 participants (7 men and 4 women) who graduated from Syrian universities. Ten of them completed the pretest, while 7 of them completed the posttest. The web-based course was found to be effective in improving the participants' EBM knowledge, skills, and attitudes at P>.05. Further, the web-based EBM course was feasible and easy-to-use. CONCLUSIONS: In order for EBM to be implemented in Syria, continuous medical education training programs should be designed for clinical practitioners. Our study shows that asynchronous web-based medical education is an effective and a feasible means for introducing the concept of EBM, improving practitioners' skills, and promoting the positive attitudes of Syrian clinical practitioners toward EBM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".