How and Why Preclerkship Students Set Learning Goals and Assess Their Achievement: A Qualitative Exploration
Bibliographic record
Abstract
PURPOSE: Health professionals are expected to routinely assess their weaknesses, set learning goals, and monitor their achievement. Unfortunately, it is well known that these professionals often struggle with effectively integrating external data and self-perceptions. To know how best to intervene, it is critical that the health professionals community understand the cues students and practitioners use to assess their abilities. Here the authors aimed to gain insights into how and why medical students set learning goals, monitor their progress, and demonstrate their learning. METHOD: In 2012, the authors conducted semistructured interviews with Year 2 students (n = 20), applying an inductive approach to data analysis by iteratively developing, refining, and testing coding structures. RESULTS: Themes were constructed through discussion and consensus: (1) Students were diverse in how they set learning goals, (2) they used a range of approaches to monitor their progress, and (3) they struggled to balance studying for exams with preparation for clinical training. Tensions observed highlight assumptions embedded in medical curricula that can be problematic. CONCLUSIONS: Educators often treat medical students as a cohesive whole, thereby creating a mismatch between assessments that are intended to be formative and information students use to monitor their progress. Despite limited exposure to clinical contexts, goal generation and monitoring often stem from a desire to prepare for clinical practice. In grappling with these tensions, it is important to be mindful that students are individualistic in how they balance their commitment to prepare for clinical work and the need to concentrate on exams.
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".