The place of evidence-based medicine among primary health care physicians in Riyadh region, Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Evidence-based medicine (EBM) is a style of practice in which doctors manage problems by reference to valid and relevant information. Unfortunately, research consistently has shown that clinical decisions rarely are based on the best available evidence. Since primary care is the essential foundation in effective health care systems, it follows that providing evidence-based primary care would reflect positively on the community's health. OBJECTIVES: Our aim was to explore the awareness and the attitude of primary health care physicians (PHCPs) towards evidence-based medicine (EBM) and determine their related educational needs. METHODS: A questionnaire study was carried out of all 650 PHCPs practising at the Ministry of Health Primary Health Care Centres in Riyadh region, Saudi Arabia. Main outcome measures were respondents' attitude towards EBM, ability to access and interpret evidence, perceived barriers to practising EBM and the best method of moving from opinion-based medicine to EBM. RESULTS: Respondents (n = 559) mainly welcomed EBM and agreed that its practice improves patient care. They had a low level of awareness of extracting journals, review publications and databases, and even if aware, many did not use them. The most commonly read journals by the PHCPs were The Practitioner and Medicine Digest. Only 16% had access to bibliographic databases and 10% to the worldwide web. The respondents showed a partial understanding of the technical terms used in EBM. The major perceived barriers to practising EBM were patient overload and lack of personal time. Respondents thought that the most appropriate way to move towards EBM was by learning the skills of EBM (43%), followed by using evidence-based guidelines developed by colleagues (37%). CONCLUSIONS: Efforts towards improving access to evidence-based guidelines and summaries are urgently needed. Teaching all the PHCPs literature searching and critical appraisal skills by feasible and friendly methods should be considered.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".