Challenges, Educational Opportunities and Barriers Faced by E.R. Board Residents During Previous Hajj Rotations
Bibliographic record
Abstract
Objectives: The prominence of using preventive health measures allowed medical residents to enhance the awareness about barriers and challenges during Hajj pilgrimage. Hajj pilgrimage usually comprises of assorted risk factors and infections that reveal the paucity of intervention programs. The study aims to examine the challenges, educational opportunities, and barriers experienced by emergency residents during Hajj rotations.Design Setting: The study has employed cross-sectional and quantitative approach to examine the challenges and educational awareness among emergency residents in Makkah, Saudi Arabia in the year 2015. Subjects: Participants were asked about the best recommended factors. Main Outcome Measure: Statistical package for social sciences (SPSS) was used to analyse the data. Frequencies were selected to analyse the obtained responses Results: Findings have asserted mixed responses toward the challenges and educational awareness among medical residents. 80% of the respondents possessed highest level of training to meet the educational goals. Moreover, majority of the residents have also stated that they were keen to start Hajj rotation. Negative responses were indicated for objectives and clinical duties by 76% residents. Responses about patients’ volume and varieties were shown positively by 83% residents.Conclusion: Saudi government should focus on these challenges and barriers to enhance the assurance level of educational programs among emergency residents.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".