Learning to navigate uncertainty in primary care: a scoping literature review
Bibliographic record
Abstract
BACKGROUND: Clinical practice occurs in the context of uncertainty. Primary care is a clinical environment that accepts and works with uncertainty differently from secondary care. Recent literature reviews have contributed to understanding how clinical uncertainty is taught in educational settings and navigated in secondary care, and, to a lesser extent, by experienced GPs. We do not know how medical students and doctors in training learn to navigate uncertainty in primary care. AIM: To explore what is known about primary care as an opportunity for learning to navigate uncertainty. DESIGN & SETTING: Scoping review of articles written in English. METHOD: Using a scoping review methodology, Embase, MEDLINE, and Web of Science databases were searched, with additional articles obtained through citation searching. Studies were included in this review if they: (a) were based within populations of medical students and/or doctors in training; and (b) considered clinical uncertainty or ambiguity in primary care or a simulated primary care setting. Study findings were analysed thematically. RESULTS: Thirty-six studies were included from which the following three major themes were developed: uncertainty contributes to professional identity formation (PIF); adaptive responses; and maladaptive behaviours. Relational and social factors that influence PIF were identified. Adaptive responses included adjusting epistemic expectations and shared decision making (SDM). CONCLUSION: Educators can play a key role in helping learners navigate uncertainty through socialisation, discussing primary care epistemology, recognising maladaptive behaviours, and fostering a culture of constructive responses to uncertainty.
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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 | 0.020 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".