Identifying a Cohort of People Who Are Transgender and Gender-Diverse Within Saskatchewan’s Administrative Health Databases
Bibliographic record
Abstract
This was a retrospective cohort study. Algorithms were developed to identify a cohort of people who were trans and gender diverse (PTGD) among provincial-level administrative health databases (physician, hospital, emergency department, and pharmacy) from April 1, 2012 to September 30, 2020. Then, healthcare usage was compared between the identified cohort and the general population. There were 6466 unique individuals identified in the cohort, out of a total population of 1.2 million Saskatchewan residents (~0.5%). They had a mean age of 42.5 (SD 17.7) years. 1946 (30.1%) had a female sex marker and 4560 (69.9%) had a male sex marker, which may not indicate their lived gender. The cohort had increased healthcare usage 2 years prior to their index date, compared to the general population, which continued to rise to 1 year past their index date across physician, emergency department visits, and hospitalizations. The results for drugs were mixed. The percentage of PTGD identified in Saskatchewan was comparable to other studies. Healthcare utilization among the cohort was higher than the general population. Further research could use external data sources to validate and improve the cohort identification methods. The large majority of individuals with a male sex marker deserves further investigation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".