Identifying a Cohort of People Who Are Transgender and Gender-Diverse Within Saskatchewan’s Administrative Health Databases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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