Understanding how physicians allocate causation in possibly work-related injury and illness
Bibliographic record
Abstract
Objectives To identify the factors that influence a family physician9s assessment of causation for compensation purposes in suspected work-related injury or illness. Methods Four groups of family physicians with differing levels of prior reporting to the Workers9 Compensation Board (zero, low, medium, and high) in Alberta, Canada, were sent four case scenarios and a series of questions related to these. For each scenario there were four different versions (SS, SW, WS, WW) with either strong or weak features suggesting work was an important cause or contributor, and either strong or weak features suggesting non-work causes or contributors. Responses to questions about causation were made on visual analogue scales. Results The nature of the condition and the scenario type (ie, the information about workplace and non-workplace factors) were both associated with the physicians9 opinions on causation. The physicians9 understanding of the nature of work. the timing of symptoms, and the patients9 implied opinion about work relatedness all appeared important in reaching a decision that a condition was work related, while the presence of other potential causes outside work seemed important in reaching a decision that a condition was not work-related. Prior reporting history of the physician was not strongly associated with opinions on work-relatedness. Conclusions The characteristics of the scenario were more important in determining physicians9 opinion about work-relatedness than the prior reporting characteristics of the physician, suggesting that it may be the case mix, rather than physician interest, that determines high or low rates of reporting.
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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 | 0.024 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".