Ambiguity and uncertainty tolerance and psychological needs of medical students: A cross‐sectional survey
Bibliographic record
Abstract
BACKGROUND: Although the ubiquitous presence of ambiguity and uncertainty in medical practice is widely acknowledged, a greater understanding of contextual factors for educators to consider in helping students learn to respond to ambiguity and uncertainty adaptively is needed. Drawing on self-determination theory, the purpose of this study was to explore the unique roles of basic psychological needs-autonomy, competence and relatedness-in medical students' tolerance of ambiguity and uncertainty. METHODS: This was a cross-sectional survey study of third-year medical students (n = 70) at a large Canadian university. In regression analysis, the three basic psychological needs were entered as predictors of medical students' tolerance of ambiguity and uncertainty while controlling for students' age and gender. RESULTS: Of the three needs, the need for competence was determined to be statistically significant in relation to students' tolerance of ambiguity and uncertainty (β = 0.326; p = 0.038). The needs for autonomy and relatedness were determined to be not statistically significant (β = -0.170; p = 0.274 and β = 0.154; p = 0.218, respectively). DISCUSSION: We observed that medical students, who experienced satisfaction of the need for competence in the learning environment, reported greater tolerance of ambiguity and uncertainty. Potential implications for medical education are discussed, based on self-determination theory.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".