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Record W2521579057

STIGMA AROUND HIV IN DENTAL CARE: PATIENTS' EXPERIENCES.

2016· article· en· W2521579057 on OpenAlexaffabout
Mario Brondani, J. Craig Phillips, Kerston Rp, Moniri Nr

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsThematic analysisStigma (botany)Dental careQualitative researchHuman immunodeficiency virus (HIV)MedicinePopulationNursingSocial stigmaHealth careFamily medicinePsychologyPsychiatryEnvironmental healthSociology
DOInot available

Abstract

fetched live from OpenAlex

Tooth decay and other oral diseases can be highly prevalent among people living with HIV/AIDS (PLWHA). Even though dental professionals are trained to provide equal and non-judgemental services to all, intentional or unintentional biases may exist with regard to PLWHA. We conducted qualitative descriptive research using individual interviews to explore the experiences of PLWHA accessing dental care services in Vancouver, Canada. We interviewed 25 PLWHA, aged 23-67 years; 21 were men and 60% reported fair or poor oral health. Thematic analysis showed evidence of both self-stigma and public stigma with the following themes: fear, self-stigma and dental care; overcoming past offences during encounters with dental care professionals; resilience and reconciliation to achieve quality care for all; and current encounters with dental care providers. Stigma attached to PLWHA is detrimental to oral care. The social awareness of dental professionals must be enhanced, so that they can provide the highest quality care to this vulnerable population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.006
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.020
GPT teacher head0.280
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2016
Admission routes2
Has abstractyes

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