Online obituaries as a complementary source of data for mortality in Canada
Bibliographic record
Abstract
BACKGROUNDObituaries and death notices have existed for centuries as a form of commemoration, particularly in Western countries.With the rise of the internet, these records have become more accessible, presenting a valuable, largely untapped source for mortality research. OBJECTIVEWe aim to collect online obituaries through web scraping and evaluate their representativeness, advantages, and limitations for use in mortality studies in Canada's two largest provinces: Quebec and Ontario. METHODSWe web scraped 236,290 and 288,623 obituaries for Quebec and Ontario, respectively, spanning the years 2017 to 2022.Using regular expressions, a formal language for defining text-search patterns, we derived demographic variables from the text to compute mortality measures, which we then compared to a gold-standard vital statistics dataset. RESULTSAlthough obituaries in Quebec and Ontario respectively account for only half and onethird of all recorded deaths, the age and gender distributions they capture closely align with those of the general population.Infant deaths remain notably underrepresented.Life expectancy estimates derived from obituaries exceed official figures by 0.4 years for women and 0.5 for men, while the modal age at death is slightly underestimated.Despite these limitations, the timeliness and demographic representativeness of online obituaries make them a valuable supplement to conventional mortality datasets in Canada.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".