Disability, mental health, sexual orientation and gender identity: understanding health inequity through experience and difference
Bibliographic record
Abstract
BACKGROUND: This paper focuses on inequities in health in the context of disability, mental health, sexual orientation and gender identity (The authors' location outside of these identities is acknowledged as a serious limitation in discussing experience as a framework to understand health inequity in the dimensions of disability, mental health, sexual orientation and gender identity). These are dimensions that lead to health inequity primarily through the pathways of stigma and discrimination. The aim here is to distinguish the unique characteristics of these groups and thereby try and articulate a new understanding of health and health equity with identity and difference in the foreground. We aim to bring attention to experience as a crucial parameter to discuss health equity in this context. DISCUSSION: Health inequity can be approached in two ways. One is to look at the lacuna in the current public health discourse in addressing the specific health concerns along the dimensions of disability, mental health, sexual orientation and gender identity. The second approach involves a more organic way of taking on board the concerns of these groups, rather than as after-thoughts; this involves a framework that gives a central role to the lived experience of stigma and discrimination. The dimensions of disability, mental health, sexual orientation and gender identity affect health inequities constitutively, instrumentally through co-morbidities, and through stigma either directly or indirectly. Experience of stigma also forms the basis of identities and the difference between identities, which emerges as an important concept in the articulation of health inequities beyond measurable gaps. Recognition and representation of these differences then form the basis of an inclusive articulation on health. CONCLUSION: The centrality of difference and experience prompts us to problematise the idea of equity that rests on 'avoidable and unfair', 'differentials' and even to argue that such a definition based on differentials, used in a quantitative sense, seriously limits our understanding of health inequity. Health equity will therefore not merely mean 'closing avoidable health gaps,' but mandate an inclusive social arrangement that celebrates difference.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".