Promoting LGBT health and wellbeing through inclusive policy development
Bibliographic record
Abstract
In this paper we argue the importance of including gender and sexually diverse populations in policy development towards a more inclusive form of health promotion. We emphasize the need to address the broad health and wellbeing issues and needs of LGBT people, rather than exclusively using an illness-based focus such as HIV/AIDS. We critically examine the limitations of population health, the social determinants of health (SDOH), and public health goals, in light of the lack of recognition of gender and sexually diverse individuals and communities. By first acknowledging the unique health and social care needs of LGBT people, then employing anti-oppressive, critical and intersectional analyses we offer recommendations for how to make population health perspectives, public health goals, and the design of public health promotion policy more inclusive of gender and sexual diversity. In health promotion research and practice, representation matters. It matters which populations are being targeted for health promotion interventions and for what purposes, and it matters which populations are being overlooked. In Canada, current health promotion policy is informed by population health and social determinants of health (SDOH) perspectives, as demonstrated by Public Health Goals for Canada. With Canada's multicultural makeup comes the challenge of ensuring that diverse populations are equitably and effectively recognized in public health and health promotion policy.
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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 | 0.035 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".