Structural determinants of stigma across health and social conditions: a rapid review and conceptual framework to guide future research and intervention
Bibliographic record
Abstract
INTRODUCTION: Stigma has been identified as a key determinant of health and health inequities because of its effects on access to health-enabling resources and stress exposure. Though existing reports offer in-depth summaries of the mechanisms through which stigma influences health, a review of evidence on the upstream drivers of stigma across health and social conditions has been missing. The objective of this review is to summarize known structural determinants of stigma experienced across health and social conditions in developed country settings. METHODS: We conducted a rapid review of the literature. English- and French-language peer-reviewed and grey literature works published after 2008 were identified using MEDLINE, Embase, PsycINFO, Google and Google Scholar. Titles and abstracts were independently screened by two reviewers. Information from relevant publications was extracted, and a thematic analysis of identified determinants was conducted to identify broad domains of structural determinants. A narrative synthesis of study characteristics and identified determinants was conducted. RESULTS: Of 657 publications identified, 53 were included. Ten domains of structural determinants of stigma were identified: legal frameworks, welfare policies, economic policies, social and built environments, media and marketing, pedagogical factors, health care policies and practices, biomedical technology, diagnostic frameworks and public health interventions. Each domain is defined and summarized, and a conceptual framework for how the identified domains relate to the stigma process is proposed. CONCLUSION: At least 10 domains of structural factors influence the occurrence of stigma across health and social conditions. These domains can be used to structure policy discussions centred on ways to reduce stigma at the population level.
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 | 0.059 | 0.112 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.034 | 0.021 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| 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".