Internal medicine residents’ achievement goals and efficacy, emotions, and assessments
Bibliographic record
Abstract
BACKGROUND: Achievement goal theory is consistently associated with specific cognitions, emotions, and behaviours that support learning in many domains, but has not been examined in postgraduate medical education. The purpose of this research was to examine internal medicine residents' achievement goals, and how these relate to their sense of self-efficacy, epistemic emotions, and valuing of formative compared to summative assessments. These outcomes will be important as programs transition more to competency based education that is characterized by ongoing formative assessments. METHODS: Using a correlational design, we distributed a self-report questionnaire containing 49 items measuring achievement goals, self-efficacy, emotions, and response to assessments to internal medicine residents. We used Pearson correlations to examine associations between all variables. RESULTS: Mastery-approach goals were positively associated with self-efficacy and curiosity and negatively correlated with frustration and anxiety. Mastery-approach goals were associated with a greater value for feedback derived from annual ACP exams, end-of-rotation written exams, and annual OSCEs. Performance-approach goals were only associated with valuing ACP exams. CONCLUSION: Mastery-approach goals were associated with self-efficacy and epistemic emotions among residents, two constructs that facilitate autonomous learning. Residents with mastery-approach goals also appeared to value a wider range of types of assessment data. This profile will likely be beneficial for learners in a competency-based environment that involves high levels of formative feedback.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".