The utility of resilience as a conceptual framework for understanding and measuring LGBTQ health
Bibliographic record
Abstract
BACKGROUND: Historically, lesbian, gay, bisexual, transgender and queer (LGBTQ) health research has focused heavily on the risks for poor health outcomes, obscuring the ways in which LGBTQ populations maintain and improve their health across the life course. In this paper we argue that informing culturally competent health policy and systems requires shifting the LGBTQ health research evidence base away from deficit-focused approaches toward strengths-based approaches to understanding and measuring LGBTQ health. METHODS: We recently conducted a scoping review with the aim of exploring strengths-based approaches to LGBTQ health research. Our team found that the concept of resilience emerged as a key conceptual framework. This paper discusses a subset of our scoping review findings on the utility of resilience as a conceptual framework in understanding and measuring LGBTQ health. RESULTS: The findings of our scoping review suggest that the ways in which resilience is defined and measured in relation to LGBTQ populations remains contested. Given that LGBTQ populations have unique lived experiences of adversity and discrimination, and may also have unique factors that contribute to their resilience, the utility of heteronormative and cis-normative models of resilience is questionable. Our findings suggest that there is a need to consider further exploration and development of LGBTQ-specific models and measures of resilience that take into account structural, social, and individual determinants of health and incorporate an intersectional lens. CONCLUSIONS: While we fully acknowledge that the resilience of LGBTQ populations is central to advancing LGBTQ health, there remains much work to be done before the concept of resilience can be truly useful in measuring LGBTQ health.
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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 | 0.059 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".