Online mentoring of medical students during COVID-19 pandemic: Another new normal
Bibliographic record
Abstract
Objectives: To determine the impact of online mentoring sessions on the students during the pandemic time. Methods: The cross-sectional descriptive study was conducted at Bahria University Medical and Dental College, Karachi. The total study duration was 5 months from March 2021 to July 2021. Quantitative research design was used. Categorical data was scored on a three point Likert scale (1= ‘Disagree’, 2= ‘Neutral’ and 3= ‘Agree’). Frequencies and percentages were calculated to determine the impact of online mentoring. Results: Sixty two percent of 2nd year MBBS students were of the opinion that online mentoring was helpful as compared to 58% 1st year and 50% 3rd year students. Students were anxious while sharing their issues online. A total of 61.66% were eager to have classes on campus as compared to online as learning difficulties were felt in 70%, 77% and 81% of 1st, 2nd and 3rd year classes respectively. Of the 1st year 39%, 2nd year 46% and 3rd year 32% showed relief after the mentoring session but were in favor of face to face sessions. Technical issues were faced by 54% 1st year, 66% 2nd year and 64% 3rd year students. Conclusion: The study suggested that students were overall satisfied with the online mentoring sessions. They do have certain apprehensions like privacy and confidentiality issues but on the whole, they considered this medium as being a powerful one in times of the pandemic. doi: https://doi.org/10.12669/pjms.38.8.5833 How to cite this:Usmani A, Imran M, Javaid Q, Tariq J. Online mentoring of medical students during COVID-19 pandemic: Another new normal. Pak J Med Sci. 2022;38(8):2125-2130. doi: https://doi.org/10.12669/pjms.38.8.5833 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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".