Creating a Powerful Platform to Explore Health in a Correctional Population: A Record Linkage Study
Bibliographic record
Abstract
We used record linkage to create a data repository of health information of persons who were federally incarcerated in Ontario and Canada. We obtained records from 56,867 adults who were federally incarcerated between January 1, 1998 and December 31, 2011 from the Correctional Service of Canada; 15,248 records belonged to individuals residing in Ontario, Canada. We linked these records to the Registered Persons Database (RPDB) which contained records from 18,116,996 individuals eligible for health care in Ontario. Out of 56,867 OMS records, 22,844 (40.2%) were linked to the RPDB. Looking only at those incarcerated in Ontario, 98%, (14 953 of 15248) records were linked to RPDB. Most records of persons in Ontario-based facilities were linked deterministically. Linkage rates were lower for women, minority groups, and substance users. In conclusion, record linkage enabled the creation of a valuable data repository: there are no electronic medical records for correctional populations in Canada, making it more difficult to profile their health.
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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 | 0.023 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".