The Hidden Curriculum of Compassionate Care: Can Assessment Drive Compassion?
Bibliographic record
Abstract
PURPOSE: Medical schools are expected to promote compassionate care among learners. Assessment is a key way to communicate values to learners but can create a hidden curriculum. Assessing compassionate care is challenging; however, not assessing it can communicate to students that such care is not valued. The purpose of this study was to explore how current assessment strategies promote or suppress the idea that caring behaviors are valued learning objectives. METHOD: Data sources were third-year course documents; interviews of 9 faculty, conducted between December 2015 and February 2016; and focus groups with 13 third-year medical students and an interview with 1 third-year medical student, conducted between February and June 2016. The stated intentions of third-year assessments were compared with the behaviors rewarded through the assessment process and the messages students received about what is valued in medical school. RESULTS: Syllabi did not include caring as a learning outcome. Participants recognized assessment as a key influence on student focus. Faculty perspectives varied on the role of medical schools in assessing students' caring and compassion. Students prioritized studying for assessments but described learning about caring and compassion from interactions such as meaningful patient encounters and both positive and negative role models that were not captured in assessments. CONCLUSIONS: Faculty members expressed concern about not assessing caring and compassion but acknowledged the difficulty in doing so. While students admitted that assessments influenced their studying, their reported experiences revealed that the idea that "assessment drives learning" did not capture the complexity of their learning.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".