HIV-Related Stigma and Overlapping Stigmas Towards People Living With HIV Among Health Care Trainees In Canada
Bibliographic record
Abstract
HIV continues to be a stigmatized disease, despite significant advances in care and concerted effort to reduce discrimination, stereotypes, and prejudice. Living with HIV is often associated with a multitude of overlapping and intersecting experiences which can, in and of themselves, also be stigmatized, and which may exacerbate HIV-related stigma. The consequences of these stigmatizing experiences are particularly impactful when the stigmatizing individual is a health care provider, as this can influence access to and quality of care. The current study empirically investigates a model of overlapping stigmas (homophobia, racism, sexism, stigma against injection drug use and stigma against sex work) potentially held by health care provider trainees in Canada to determine how these constructs overlap and intersect, and to assess whether HIV-related stigma may have unique attributes. Understanding overlapping stigmas can help inform targeted, stigma-informed training for health care trainees in order to provide effective, compassionate care for people living with HIV.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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, 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".