Do expert assessments converge? An exploratory case study of evaluating and managing a blood supply risk
Bibliographic record
Abstract
BACKGROUND: Examining professional assessments of a blood product recall/withdrawal and its implications for risk and public health, the paper introduces ideas about perceptions of minimal risk and its management. It also describes the context of publicly funded blood transfusion in Canada and the withdrawal event that is the basis of this study. METHODS: Interviews with 45 experts from administration, medicine, blood supply, laboratory services and risk assessment took place using a multi-level sampling framework in the aftermath of the recall. These experts either directly dealt with the withdrawal or were involved in the management of the blood supply at the national level. Data from these interviews were coded in NVivo for analysis and interpretation. Analytically, data were interpreted to derive typifications to relate interview responses to risk management heuristics. RESULTS: While all those interviewed agreed on the importance of patient safety, differences in the ways in which the risk was contextualized and explicated were discerned. Risk was seen in terms of patient safety, liability or precaution. These different risk logics are illustrated by selected quotations. CONCLUSIONS: Expert assessments did not fully converge and it is possible that these different risk logics and discourses may affect the risk management process more generally, although not necessarily in a negative way. Patient safety is not to be compromised but management of blood risk in publicly funded systems may vary. We suggest ways of managing blood risk using formal and safety case approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".