“I went in there, had a bit of an issue with those folks”: everyday challenges of heterosexual African, Caribbean and black (ACB) men in accessing HIV/AIDS services in London, Ontario
Bibliographic record
Abstract
BACKGROUND: In Canada, heterosexual African, Caribbean, and Black (ACB) men's heightened risk of HIV infection has been linked to behavioral characteristics, including practices of hegemonic masculinity that discourage the use of HIV preventive services. However, this framing is bereft of the role of structural factors that may be contributing to new HIV infections. This paper examined the underlying factors limiting access to health services among heterosexual ACB men in London, Ontario Canada. METHODS: A convenient sampling technique was used to recruit thirty-seven (n = 37) self-identified heterosexual ACB men and service providers. Four focus groups (FG) were conducted; three with ACB participants of similar age category (i.e., 16-24; 25-38; 39+), and one with service providers. The FGs focused on the barriers to using health services and interrogated the ease of access to HIV intervention programs by ACB men respectively. Recurring themes from the FGs were probed further using in-depth interviews (n = 13). FGs and in-depth interviews complemented each other in reducing uneven power dynamics, fact checking, and allowing for detail discussion of the topic under study. Data analyses were done in NVivo using a mixed inductive-deductive thematic analyses approach. RESULTS: Most ACB men lacked information on HIV and were unaware of their increased risk of infection. Contrary to the notion that behavioral characteristics keep ACB men away from health services, we found that most ACB men were unaware of the availability of these services. Those that had some knowledge about the services reported that they were not appropriately tailored to their needs. In addition, stereotypes and stigma about the etiology of HIV among Blacks, and systemic neglect served as significant barriers to ACB men's use of services. CONCLUSION: The findings suggest that, to enhance preventive health service use among heterosexual ACB men, there is the need to remove structural barriers. Engaging ACB men in the design and implementation of policies may be useful at improving access to HIV information, testing, and treatment services. Increased information dissemination to ACB men would create awareness of the availability of HIV services. Finally, service providers should be conscious of ACB men's concern about experiences of discrimination and racism at service centers.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".