How physicians allocate causation: a scenario study with factorial design
Bibliographic record
Abstract
BACKGROUND: Causation is a complex concept but important in suspected work-related disease. Physicians routinely make initial assessments of causation as part of their work, but the factors contributing to these assessments are not well understood. AIMS: To determine which factors influence a family physician's assessment of causation when seeing patients with suspected work-related injury or illness. METHODS: Four groups of family physicians with differing levels of prior reporting (zero, low, medium, high) to the Workers Compensation Board received a questionnaire including four randomly allocated case scenarios. For each scenario there were four versions with either strong or weak causal features suggesting work or non-work factors were important causes or contributors. Responses to questions were made on a series of visual analogue scales. RESULTS: The nature of the condition and scenario type (i.e. strength of the causal information about workplace and non-workplace factors) were associated with the physicians' opinion on work-relatedness. Understanding the nature of the patient's work, the timing of symptoms and the patients' opinion about work-relatedness were viewed by the physicians as important. A decision that a condition was not work related was influenced primarily by the strength of potential causes outside work. Prior reporting history of the physician was not associated with opinions on work-relatedness, nor the factors considered in reaching this decision. CONCLUSIONS: The characteristics of the case scenario were more important in determining a physician's opinion about work-relatedness than the characteristics of the physician.
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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 | 0.070 | 0.220 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".