Twelve Tips to Train Medical Students to Manage Their Uncertainty and to Provide Reassurance to Patients and Their Caregivers
Bibliographic record
Abstract
Background: Uncertainty is pervasive in clinical medicine and provides a major hurdle for decision-making. Our previous work has demonstrated the importance of providing parents of paediatric patients reassurance in the medical team's plan of action, what we have termed "comfort with doctors' reassurance." Aims: We provide several practical and implementable tips to medical students in how they can learn to deal with their own uncertainty, recognize the subtle complexities involved and acknowledge the emotional stress that can accompany this process, develop a healthy long-term relationship with uncertainty, and ultimately embrace its potential in providing holistic care to their patients and their caregivers. Results: Twelve tips and actions in four main domains are recommended: (A) Understanding and Integrating Uncertainty into Medical Education, (B) Building Resilience and Empathy Through Self-Regulation and Reflection, (C) Enhancing Communication and Relationship-Building Skills, and (D) Clinical Skills for Reassurance and Decision-Making. Conclusions: Even though uncertainty in medical decision-making is pervasive and challenging for the medical trainee, there are several concrete strategies that can build comfort with uncertainty in trainees and reinforce the potential positive attributes of uncertainty in the holistic care of patients. Empathy and expressing compassion remain key skills in bridging caregiver discomfort with uncertainty and comfort with doctors' reassurance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".