A Case-control Study on Personal and Academic Determinants of Dropout among Health Profession Students
Bibliographic record
Abstract
An adequate number of healthcare providers is an essential factor in the prosperity of a population. One challenge faced by universities is student dropout. This case-control study aimed to examine the academic, psychological, medical, social, as well as female-related risk factors at a health-sciences university in Saudi Arabia in the academic year 2016-2017. The study included a total of 723 students, of whom 143 dropped out. A validated questionnaire was used to assess risk factors. Comparisons were made using chi-square test with the outcome of interest being dropout at the end of the academic year. Around 20% of students had dropped out by the end of the academic year 2016-2017. Significant risk factors for dropout included male gender, lack of previous university degree, having a primary as well as a secondary specialty choice, not matching into the first specialty choice, English language, and female-related risk factors, such as pregnancy. Health-care education is an inherently stressful environment where dropout is a concerning phenomenon. It is imperative to recognize risk factors and develop strategies to ensure students’ successful adaptation and progress. Policymakers should be aware of the impact of academic and gender-related factors to address and help limit the number of students dropping out of highly needed professions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".