Assessing and Improving Study Skills Support in Medical Education Through a Student-Staff Partnership: Mixed Methods Approach
Bibliographic record
Abstract
Background: The necessity for self-regulated, lifelong learners in the rapidly evolving field of medicine underscores the importance of effective study skills. Efforts to support students with these skills have had positive outcomes but are often limited in scope and accessibility, with a tendency to target groups facing immediate challenges. Objective: This study aimed to explore the student perspective on study skills support at University College London Medical School through a student-staff partnership, with the goal of guiding future improvements. Methods: A mixed methods approach was adopted using an anonymous questionnaire and focus groups. After analyzing questionnaire responses using descriptive statistics to refine focus group questions, focus groups were conducted to delve deeper into identified issues. Transcripts were analyzed thematically using inductive coding. Results: In total, 116 students completed the questionnaire in full and 6 students participated in 2 focus groups. The questionnaire revealed that 68% (68/100) of respondents felt that they never received study skills support at University College London Medical School. Preferred methods of support included small group sessions (56/100, 56%) and topics like examination preparation (83/100, 83%) and study skills specific to medicine (72/100, 72%). Focus group themes were the lack of current study skills support, delivery of study skills support, specific study skills for medical school, personalized approach to support needed, and accessing support. Findings informed the co-creation of study skills resources. Conclusions: Overall, the findings highlight the need for strategically incorporating study skills support at medical school, emphasizing early and consistent promotion and tailored delivery methods.
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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 | 0.050 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 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".