The damaging legacy of damage‐centered LGBTIQ+ research: Implications for healthcare and LGBTIQ+ health
Bibliographic record
Abstract
Abstract Research on LGBTIQ+ populations has focused primarily on identifying problems in the community (e.g., health disparities) and their predictors (e.g., minority stressors, discrimination). Scholars have argued that the approach of highlighting “damage” or deficits has been helpful for advocacy but has also harmed this community by perpetuating stereotypes (e.g., LGBTIQ+ individuals are unhealthy), ignoring or devaluing positive LGBTIQ+ experiences, and contributing to negative interactions in healthcare settings. To evaluate the extent to which a damage‐centered approach dominates the body of available research, the authors of this article conducted a content analysis of articles related to LGBTIQ+ health published in the Journal of Social Issues (JSI). The content analysis of 45 years of published manuscripts (1978–2023) revealed a strong emphasis on damage‐centered themes. In response, this article advocates for structural changes that may lead to an increase in research that focuses LGBTIQ+ experiences more holistically, with the overarching goal of reimaging LGBTIQ+ research. Such suggested changes include concentrated research funding and publishing opportunities, medical training that emphasizes a strengths‐based focus, and function‐oriented and autonomy‐promoting LGBTIQ+ research. This article suggests strategies to improve patient‐provider interactions in healthcare and enhance the overall well‐being of LGBTIQ+ communities. It advocates for a deliberate expansion towards a more holistic, less damage‐centered body of research in LGBTIQ+ psychology.
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.
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 | 0.074 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".