Leveraging Health Administrative and Qualitative Data to Understand Mental Health Experiences of Transgender and Gender Diverse People: An Explanatory Sequential Mixed Methods Study
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
Mixed methods research (MMR) studies using health administrative data (HAD) coupled with qualitative methods can offer unique insight into the health inequities experienced by marginalized populations. However, little guidance exists on how and why to mix HAD and qualitative research. This methodology paper uses the real-life experiences of conducting an explanatory sequential mixed methods study to discuss methodological considerations when combining health administrative and qualitative data for equity-oriented research. This study focused on access to mental healthcare for transgender and gender diverse (TGD) individuals in Ontario, Canada. We illustrate the foundational importance of paradigmatic considerations, theory, and reflexivity in the research process; providing practical examples of their impact on data collection, analysis, and integration in such a study.
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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 | 0.052 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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