Supporting choices about HIV disclosure in the workplace: A cross-Canada survey of strategies
Bibliographic record
Abstract
BACKGROUND: People living with human immunodeficiency virus (HIV) often make highly personal decisions about whether or not to disclose their HIV status in the workplace. OBJECTIVE: We aimed to determine current practices that support people living with HIV to make workplace disclosure decisions and to understand factors that affect disclosure decision-making. METHODS: Ninety-four people who provide health, social and peer-based services responded to an on-line survey about their experiences supporting workplace disclosure decisions of employees living with HIV. RESULTS: Respondents identified a range of strategies to support workplace disclosure decision-making. One-third of respondents were only a little or not confident in their abilities to support people in making disclosure decisions and 32% expressed little or no confidence in the resources available. Respondents working at HIV-specific organizations, as compared to respondents not working at those organizations, were more confident supporting people with disclosure decisions and in available resources, p < .05. Perceived barriers to disclosure decisions included stigma, lack of knowledge, and personal factors. Supports for decision-making resided within personal, workplace and societal contexts. CONCLUSIONS: The study provides important understanding about the complexity of disclosure decision-making and strategies that people living with HIV can use to address this complex issue.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".