Towards Embracing Clinical Uncertainty
Bibliographic record
Abstract
• Summary: The oral transmission and transformation of client information in an apprenticeship setting provides a rich environment in which to observe students and their expert supervisors managing uncertainty. In this Canadian-based study, we examined the communicative features of 12 social work supervisions involving social work students and their supervisors and enriched our observations with subsequent interviews of the participants. • Findings: Social work students viewed the acknowledgement and examination of uncertainty as a touchstone of competent social work. This observation contrasted with our past study of medical and optometry students who focused on personal deficit and a distrust of acknowledging uncertainty. Our observations and interviews revealed a unique professional signature to the novice rhetoric of uncertainty (seeking guidance, deflecting criticism, owning limits, showing competence) that suggests differing professional identities and contextual settings. • Applications : An attitudinal shift toward accepting and trusting uncertainty in medicine and optometry may facilitate an enriched educational environment for students and a more open dialogue with patients about uncertainty. The unique professional signatures of this rhetoric offer insights into how professional identity shapes attitudes and behaviors toward uncertainty and suggest a source of tension within interdisciplinary healthcare teams.
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 | 0.047 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.014 |
| 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".