How Do Paediatricians Manage Comfort with Uncertainty in Clinical Decision-Making
Bibliographic record
Abstract
Background: While healthcare practice is inherently characterised by uncertainty, there is a paucity of formal curricular training to support comfort with uncertainty (CWU) in postgraduate training. Indeed, some evidence suggests medical training inherently conflicts with CWU in emphasizing pedagogies focussing on "fixing" the problem. While referral patterns increase significantly, dealing with uncertainty has direct implications for patient referral rates and use of valuable healthcare resources. Methods: Paediatricians in Ireland were invited to participate. Face-to-face interviews were conducted after participants watched videos of varied clinician-patient interactions.. Two researchers independently analysed the collected data using thematic analysis. Triangulation and member checking was performed to ensure validity of findings. A reflection journal documented the research journey. Results: Thirty four paediatricians participated. Five themes were identified: the interplay between quality of information, uncertainty and decision-making, confidence in clinical assessment and first-hand patient evaluation, anxiety and fear experienced by medical professionals when dealing with complex and serious conditions, strategies employed by medical professionals in managing their own uncertainty and the impact of societal and parental expectations on medical decision-making. These are moderated by a number of factors, most significantly the child's caregivers' comfort with doctors reassurance (CDR). Enacted management will diverge from the consultant's clinical plan when the caregiver's CDR cannot be satisfactorily supported. Discussion: Clinician CWU in the paediatric context is inextricably linked to caregiver CDR. The complexities and central importance of social context in understanding CWU has important implications for how we develop educational activities to support clinician CWU and patient/care-giver CDR. This may translate to efficient use of limited resources in healthcare settings.
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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 | 0.019 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".