The impact of low self-esteem on academic achievement and the behaviors related to it among medical students in Saudi Arabia
Bibliographic record
Abstract
OBJECTIVES: To measure the prevalence of low self-esteem among medical students in Saudi Arabia and determine its impact on their behaviors and academic achievement. METHODS: We hypothesized that the level of self-esteem reflected on the student's academic performance and linked to some of their behaviors. A cross-sectional study was carried out among students of the medical colleges in Saudi Arabia. A self-administered questionnaire was distributed electronically using social media platforms, socio-demographic data, Rosenberg's self-esteem scale, and a questionnaire about self-esteem-related behaviors. RESULTS: Of 1099 participants (55.9% females and 50% males), 24.1% showed low self-esteem. Independent significant predictors of low self-esteem were female gender and diagnosis with mental illness. Increasing GPA was associated with better self-esteem. Participating in students' study groups and attending self-development programs were estimated to be the protective factors against low self-esteem. CONCLUSION: One-quarter of medical students are assumed to have low self-esteem. Improved GPA ratings positively influence self-esteem, while attending students' study groups and self-development programs were identified as protective factors for low self-esteem. Further studies are needed to shed more light on this important topic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".