Assigning Medical Students Learning Goals: Do They Do It, and What Happens When They Don't?
Bibliographic record
Abstract
Theory: Medical curricula now include more time for trainees to manage their studying independently, yet evidence suggests that time is not well spent without guidance. Social-cognitivist models of self-regulated learning suggest value when guiding learners to set goals related to their performance processes (actions producing outcomes) versus their performance outcomes (products of performance). Hypotheses: We expected participants oriented to set process goals would demonstrate better suturing skill retention compared with participants oriented to set outcome goals. Method: We randomly assigned 41 medical students to two groups: outcome oriented or process oriented. They self-scored their performance using a visual analog scale on every third trial during 25 training trials, and during 10 retention trials 2 weeks later. Two raters assessed participants’ suturing performances (process) and final products (outcome). After finding weak support for our hypothesis, we calculated a “self-monitoring calibration coefficient” as the Pearson’s correlation between the raters’ average score and each participant’s self-scores. We used a mixed-effects analysis of variance to compare participants’ performance scores as well as t tests and an analysis of variance to compare their self-monitoring calibration coefficients. Results: Analysis of skill retention data revealed a significant Group × Trial interaction, suggesting a benefit for the process group only for the 10th retention trial (p = .03). During training, the process group had significantly better (p = .02) self-monitoring calibration (r = .71 ± .29) than the outcome group (r = .38 ± .55). In retention, participants in both groups were significantly better calibrated (p = .04) with rater’s scores of performance processes (r = .39 ± .60) versus performance outcomes (r = .11 ± .63). Conclusions: Our findings provide limited evidence for our original hypothesis. Perhaps more important, however, our self-monitoring calibration data highlighted inconsistencies between our interventions and our participants’ apparent preferences. Not all participants adopted their assigned goal setting orientation, showing that researchers and educators must consider the extent to which trainees adopt imposed instructions in any educational intervention.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".