Dental care for people living with HIV, a phenomenological approach
Bibliographic record
Abstract
Background: Oral health care is amongst one of the highest unmet needs of people living with HIV/AIDS (PLHIV); this may be due to barriers they face accessing care, such as stigmatization and fear of discrimination. PLHIV have indeed reported negative experiences at dental offices and with dental staff. However, there is a lack of recent and in-depth studies that capture the current perspectives of PLHIV regarding accessing dental services. In order to respond to the oral health needs of PLHIV, it is thus important to better understand how they access and experience dental care in Canada. Objectives: Our objective was to better understand the lived experiences of PLHIV with respect to accessing dental care. In particular, we sought to better understand the difficulties and the stigmatization they faced trying to fulfil these needs, and finally to make recommendations for alleviating these difficulties. Methods: We adopted a participatory approach and an interpretive phenomenological research design. We invited community associations for PLHIV and organizations fighting HIV to collaborate with us at all stages of the research. In order to gain an in-depth understanding of the perspectives and experiences of PLHIV, we used a qualitative approach, namely interpretive phenomenology, which is particularly appropriate for understanding and describing complex and sensitive experiences. We conducted in-depth interviews with eight people living with HIV in Montreal. The interviews were audio-recorded, transcribed verbatim, and interpretively analysed.Findings: Living with HIV significantly shapes the experiences of people regarding their oral health and accessing dental care. Our participants struggled with an imperfect oral health and a limited access to dental care. Thus they experienced anxiety over managing a fragile oral health for years to come. Although they were generally satisfied with their dentists, they reported isolated negative encounters with the dental staff. Because of these negative experiences, in addition to that of other PLHIV, some participants anticipated being stigmatized in dental settings. To avoid such potential discrimination, they either chose not to disclose their HIV status to the dentists, or to visit a trusted dentist known for accepting PLHIV.Conclusion: Dental professionals should be aware of and sensitive to the complexities of PLHIV's life experiences and try to accommodate their specific needs. Dentists alongside other members of the society should also tackle HIV stigma in dental settings and society at large.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".