Using Death Certificates and Medical Examiner Records for Adolescent Occupational Fatality Surveillance and Research: A Case Study
Bibliographic record
Abstract
Death certificates and medical examiner records have been useful yet imperfect data sources for work-related fatality research and surveillance among adult workers. It is unclear whether this holds for work-related fatalities among adolescent workers who suffer unique detection challenges in part because they are not often thought of as workers. This study investigated the utility of using these data sources for surveillance and research pertaining to adolescent work-related fatalities. Using the state of North Carolina as a case study, we analyzed data from the death certificates and medical examiner records of all work-related fatalities data among 11- to 17-year-olds between 1990-2008 (N = 31). We compared data sources on case identification, of completeness, and consistency information. Variables examined included those on the injury (e.g., means), occurrence (e.g., place), demographics, and employment (e.g., occupation). Medical examiner records (90%) were more likely than death certificates (71%) to identify adolescent work-related fatalities. Data completeness was generally high yet varied between sources. The most marked difference being that in medical examiner records, type of business/industry and occupation were complete in 72 and 67% of cases, respectively, while on the death certificates these fields were complete in 90 and 97% of cases, respectively. Taking the two sources together, each field was complete in upward of 94% of cases. Although completeness was high, data were not always of good quality and sometimes conflicted across sources. In many cases, the decedent's occupation was misclassified as "student" and their employer as "school" on the death certificate. Even though each source has its weaknesses, medical examiner records and death certificates, especially when used together, can be useful for conducting surveillance and research on adolescent work-related fatalities. However, extra care is needed by data recorders to ensure that occupation and employer are properly coded when dealing with adolescent worker deaths.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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 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".