SOGIE-diverse inclusivity: An analysis of 2SLGBTQ+ representation on Ontario, Canada public health unit websites
Bibliographic record
Abstract
Objectives: The objectives of this study were to: 1) examine how often sexual orientation, gender identity and expression (SOGIE)-diverse terms appeared on websites of public health units (PHUs) in Ontario, Canada, and 2) determine the contexts within which these terms appeared on these PHU websites. Study design: This study employed a summative content analysis of 34 Ontario PHU websites. Methods: The study involved compiling a list of SOGIE-diverse terms, searched PHU websites using their integrated search systems, and retrieved webpages where relevant search terms were found. The data analysis included enumerating how often the SOGIE-diverse terms appeared and summarizing the contexts within which the terminology appeared on the 34 PHU websites. Results: Across these websites, terms commonly found were variations of "LGBT," "sexual/sexuality," and identity terms, including "bisexual," "gay," "queer," and "lesbian." SOGIE-diverse communities were reflected in two broad categories: sexual health and infectious diseases; and mental health, SOGIE-diversity discrimination, and resources. Conclusions: In this website review, most PHUs offer some relevant information about and for SDCs; however, they also lacked in specific, SOGIE-relevant ways. While sexual health and mental health are pertinent concerns, this myopic view of relevant health concerns for SOGIE-diverse communities excludes holistic health conceptualizations and lacks nuanced understanding of SDCs' rich lived experiences. The implications of this work are to ensure that PHUs have mutual understanding of SOGIE-diversity related terms, and how they represent SDCs via their websites and the information and services offered for these communities. Ontario PHUs, especially through public-facing platforms such as websites, have a unique opportunity to educate both SDCs and the broader public. Clear, consistent, and collaborative communication can minimize inequitable health outcomes for these communities.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".