Penalized Regression Methods for Modelling Rare Events Data with Application to Occupational Injury Study
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Bibliographic record
Abstract
Occupational injuries are a serious public health concern for workers around the world.\nAmong all occupational injuries reported to the Workers' Compensation Board of Saskatchewan\n(WCB-SK) from 2007-2016, 177 (0.06%) out of 280,704 injury claims were fatal. Although\nwork-related injuries are relatively rare, they have tremendous impact on the workers, their family, as well as a company's overall productivity, hiring/training costs, and insurance premiums. To help inform prevention of fatal claims, this study identified factors that increase\nthe probability of fatal injury claims in Saskatchewan.\n\n WCB Saskatchewan's administrative occupational injury claims data from 2007-2016 was\nused to extract fatal and non-fatal occupational events. Potential covariates included worker\ncharacteristics (age, gender, occupation) and incident characteristics (source of injury, cause\nof injury, part of body). Given the fatality being rare in this study, conventional logistic\nregression including multiple categorical covariates with over 40 parameters yielded biased\nparameter estimates. Penalized logistic regression methods, such as bias-correction method,\ni.e. Firth's method as well as the model selection methods, i.e., lasso and elastic net were\ncompared to identify an optimal modelling strategy for calculating the odds ratio (OR) and\n95% confidence intervals (CI) for probability of a WCB claim being fatal (vs. non-fatal).\n\n Based on the best-fitting model, i.e., Firth's logistic regression of the selected variables\nunder the elastic net method, odds of a claim being fatal was 5.5 (95% CI: 2.77,12.46) times\nhigher among men than women and was 6.59 (95% CI: 3.59,12.20) times higher for seniors\naged 65-85 as compared with those who are aged 14-24. Odds of a claim being fatal among\nthose who work in primary industry is 2.85 (95% CI: 1.07,9.39) higher than those working\nin social sciences. The odds of injury being fatal for machinery sources is 51 (95% CI:\n10.38,505.38) times higher than chemical products as the source.\n Men workers are at higher risk of a claim being fatal (vs non-fatal). With respect to\nage, result of analysis showed that the middle-aged workers are at a lower risk, and the\nyoung workers are at a higher risk than middle aged workers. The risk of a claim being fatal\nincreased sharply as age increased from 45 to 85. Primary industry sector and machinery have\na disproportionate share of fatal claims. This knowledge can improve workplace safety by learning from past incidents, identifying significant risk factors, and implementing targeted\nprevention strategies. Through development of effective interventions, we hope to prevent\nfatal injuries in Saskatchewan.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it