Individual and Institutional Factors Preventing Completion of Research by Medical Graduate Students at Cairo University: Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Medical research plays a significant role in advancing the level of health care worldwide. This research is a crucial part of the development of any educational system. In developing countries, the publication rate related to the medical sciences is lower than that in developed countries. OBJECTIVE: The aim of this study was to explore the causes of delay in publishing research and the factors that hinder the completion of master's degree projects in a group of medical graduate students at Cairo University Faculty of Medicine. METHODS: A web-based questionnaire was introduced to approximately 150 medical graduates in different specialties through social media. The questionnaire aimed to investigate the reasons for delays in publishing master's degree manuscripts after graduation among a group of medical graduates. RESULTS: Of the graduates contacted, 130 responded to the web-based survey. The ages of the participants ranged from 23-38 years (SD 3.88); 72 of them were male, and 58 were female. Causes of noncompletion of manuscripts were analyzed; lack of proper research training and the absence of supportive mentorship were top reasons. We found a significant relationship between being married and failing to complete the assigned project from its start up to publication. Moreover, we found that the frequency of nonfulfillment increased among those who experienced poor mentorship. CONCLUSIONS: Several factors are contributing to the delay in publication of medical manuscripts related to research projects by medical graduates of the Cairo University Faculty of Medicine. Pensive supervision must be implemented to decipher the persistent institutional problems that obstruct research progress.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".