Resilience among Undergraduate Medical Students of a Medical College of Eastern India
Bibliographic record
Abstract
Background: Resilience is a dynamic capability which helps people to survive on challenges given appropriate social and personal contexts. Objective: To determine the resilience and to find out the association between resilience and selected socio-demographic variables, if any. Methodology: An institution based mixed method study was conducted among MBBS students from April to May, 2018 at IQ City Medical College by using Child and Youth Resilience Measures Questionnaire (CYRM-28). Multivariate analysis of variance (MANOVA) was performed using SPSS-21software. Results: Older students, Males, 8th semester batch, and day boarder were more resilient in certain areas. In individual and contextual domain, score gradually increased with increase in age groups. Resilience score were more or less similar among both the sexes. 8th semester students were found to be more resilient in all the domains. Personal skills, social skills, psychological care giving, education, played much larger role in differences across the sex, age, semester, and accommodation. Conclusion: Thus counselling and more teacher student bonding are required to propagate the resilience of the medical students. More emphasis has to be given on identified areas so as to make our future doctors more strong and resilient.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".