Self-directed learning readiness of Indian medical students: a mixed method study
Bibliographic record
Abstract
BACKGROUND: Self-directed learning (SDL) is defined as learning on one's own initiative, with the learner having primary responsibility for planning, implementing, and evaluating the effort. Medical education institutions promote SDL, since physicians need to be self-directed learners to maintain lifelong learning in the ever-changing world of medicine and to obtain essential knowledge for professional growth. The purpose of the study was to measure the self-directed learning readiness of medical students across the training years, to determine the perceptions of students and faculty on factors that promote and deter SDL and to identify the role of culture and curriculum on SDL at the Christian Medical College, Vellore, India. METHODS: Guglielmino's SDL Readiness Scale (SDLRS) was administered in 2015 to six student cohorts (452 students) at admission, end of 1st, 2nd, 3rd and 4th year of training, and at the beginning of internship in the undergraduate medicine (MBBS) program. Analysis of variance (ANOVA) was used to compare SDL scores between years of training. 5 student focus groups and 7 interviews with instructors captured perceptions of self-direction. Transcripts were coded and analyzed thematically. RESULTS: The overall mean SDLRS score was 212.91. There was no significant effect of gender and age on SDLR scores. There was a significant drop in SDLRS scores on comparing students at admission with students at subsequent years of training. Qualitative analysis showed the prominent role of culture and curriculum on SDL readiness. CONCLUSIONS: Given the importance of SDL in medicine, the current curriculum may require an increase in learning activities that promote SDL. Strategies to change the learning environment that facilitates SDL have to be considered.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".