P2-S6.13 Gay men's assessment of sexual and social risks in the context of a recent HIV-positive diagnosis
Bibliographic record
Abstract
Background Technological innovations in HIV testing that allow for the diagnosis of very recently-acquired HIV infections provide opportunities to understand sexual and social risk perceptions before and after an HIV-positive diagnosis. Longitudinal interviews with a group of gay men who have received an early or acute HIV diagnosis represent an important opportunity to understand risk assessment beyond individual-level paradigms of risk analysis and to broaden our understanding of social and structural risk factors associated with HIV infection, diagnosis and disclosure. Methods Study recruitment is being conducted through six clinical sites in British Columbia, Canada (April 2009—December 2012) by the CIHR Team in the Study of Acute HIV Infection in Gay Men. Participants (n=12 at time of analysis) completed a series of self-administered questionnaires and semi-structured face-to-face interviews. Baseline qualitative interviews were recorded, transcribed verbatim and analysed. A thematic analysis, informed by a social organization of knowledge perspective, was conducted. Results Three interrelated domains of risk assessment emerged from the interviews. First, we explicate how men calculated the epidemiological or sexual risks of transmitting HIV before and after their diagnosis, and how such an assessment informed their sexual behaviours. Second, men described a myriad of experienced and perceived social risks, such as stigma and rejection, associated with the disclosure of their HIV-status to their family, friends, colleagues and intimate partners. Third, men identified potential problems with technologies of status notification which create a set of institutional risks related to the processes by which patients learn of their HIV-positive status. The relationship between these textually-mediated fields of risk is examined. Conclusion A stratified conception of risk allows us to understand the everyday situations in which people assess HIV-related 'dangers' in their social and sexual lives. This formative research has important implications for educational campaigns on HIV transmission risk assessment for both HIV negative and positive gay men. This work can also inform counselling and support services to address how disclosure risks are negotiated during an early or acute HIV diagnosis. Important implications for clinical and public health practices, including how and when people are given their HIV diagnosis, are raised.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".