Students’ Perception of Peer- Students Mentoring Program “Big Sibling Mentoring Program” to Complement Faculty Mentoring of First-Year Medical Students in Saudi Arabia
Bibliographic record
Abstract
Abstract Background The Big Sibling Program is an innovative peer student mentoring method that was designed and implemented by the students’ council in 2021 to complement the faculty mentoring program of first year medical students in the College of Medicine, King Saud University, Saudi Arabia. The aim of the study was to evaluate the medical students’ perception of the peer students ‘Big Sibling’ mentoring program and to assess the effectiveness of the program in terms of changes in the behavior and academic performance of the students.Methods This is a retrospective study that was conducted in 2021 in the college of medicine, King Saud University. A registration form that includes demographic data, personal information and academic performance (GPA and extracurricular achievements) was sent to all second- and third-year medical students to select the Big Siblings. A total of 49 mentors ‘Big Siblings’ were accepted (30 male, 19 female) and matched with the little siblings from first year medical students. A written consent was obtained from the participants. The survey was structured on a 5-point Likert scale, and composed of four sections of closed-ended questions, that includes: the demographic data, the students’ (little sibling) evaluation of the mentorship sessions, the little sibling perception of the Big Sibling Program and its effectiveness of the Big Sibling Program in terms of behavioral or quality effects.Results Out of 297 first year medical students, 284 students responded with a response rate of 95.62%. The majority of the students (n = 268, 94.36%, p < 0.001) significantly agreed that the Big Sibling was readily available and have personally benefitted from the relationship with the mentor (big sibling) (n = 256, 90.14%, p < 0.001). The students significantly disagree that the relationship with the Big Sibling requires too much time (n = 206, 72.54%, p < 0.001) and that they do not need a mentor (n = 224, 78.87%, P < 0.001). The majority of the students significantly agreed that mentoring is a good idea (n = 268, 94.37%, p < 0.001), the Big Siblings Program helped reduce their stress (n = 240, 84.51%, p < 0.001), helped to adjust to their new college environment (n = 254, 89.44%, p < 0.001), helped them to advance academically (n = 223, 78.52%, p < 0.001). encouraged them to become involved in college extracurricular activities (n = 165, 58.10%, p < 0.0001), encouraged them to become involved in research activities (n = 123, 43.31%, p < 0.001), encouraged them to become socially engaged with students in my batch at the college of Medicine (n = 203, 71.48%, p < 0.001), encouraged them to become socially engaged with students in elder batches at the college of Medicine (n = 203, 71.48%, p < 0.001). The majority of the students think that the Big Sibling program significantly (p < 0.001) improved their self-confidence (n = 210, 73.94%, p < 0.001), self- awareness (n = 240, 84.51%), accountability and responsibility (n = 240, 54.51%), leadership (n = 156, 54.93%), resilience (n = 202, 71.13%), punctuality (n = 196, 69.01%, p < 0.001), ability to manage time properly (n = 215, 75.70%), ability to cope with stress (n = 221, 77.82%), ability to solve problems (n = 218, 76.76%) and the ability to work in a team (n = 214, 75.35%).Conclusions First year medical students have positive perception of the peer students ‘Big Sibling program’ and its effectiveness in terms of behavior and academic achievements.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".