Challenges Facing Clinical Research: An Example From a Middle Eastern Country
Bibliographic record
Abstract
INTRODUCTION: Most countries thrive to build and expand national research. Challenges may hinder clinical researchers and potential researchers particularly in developing countries. We aim to study some of these challenges in the Kingdom of Saudi Arabia (KSA). METHODS: A questionnaire reflecting possible challenges was completed by delegates and faculties of 5 cancer related educational scientific meetings.RESULTS: One hundred and forty seven responders were practicing in KSA and are the subject of this report. Ninety five (64.6%) were physicians. While 122 (83%) are interested in conducting research, service commitments and inadequate time, process of research approval within the department and obtaining financial funding were the most frequent challenges reported by 86 (58.5%), 61 (41.5%) and 53 (36.1%) of responders.CONCLUSION: Major challenges hinder clinical research development. The identified challenges need to be seriously addressed if advances in clinical research are to be expected.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".