Attitudes of intern doctors towards people living with HIV: a cross-sectional study in Türkiye
Bibliographic record
Abstract
Abstract Background People living with HIV (PLWH) may experience exclusion from various groups such as family members, employers, and health professionals. These negative attitudes can lead to depression, anxiety, and delay in treatment in PLWH. We aimed to determine attitudes towards PLWH and related factors among intern doctors. Methods We conducted a cross-sectional study in July-August 2023 at a faculty of medicine. The study group consisted of 201 intern doctors. Data were collected through a structured questionnaire including sociodemographic and related variables, the AIDS Attitude Scale (AAS) and the Toronto Empathy Questionnaire. Multiple linear regression analysis was performed to identify factors associated with attitudes towards PLWH. Results The mean age was 23.4±1.0 years, and 55.2% were male. The mean AAS score was 53.6±4.9, indicating a moderate attitude toward PLWH. Two-thirds of the students reported that the information given about HIV during their medical education was inadequate. Furthermore, the majority of participants (72.6%) reported informing other healthcare professionals about the patient's HIV status without their consent. Multivariate analysis revealed that more positive attitudes were significantly associated with higher income (β = 0.15; p = 0.020), not thinking that additional precautions were needed in the care of PLWH (β = 0.20; p = 0.007) and not warning other staff against the patient's wishes (β = 0.15; p = 0.031). Higher empathy levels were positively associated with better attitudes (β = 0.19; p = 0.004). Conclusions Overall, the study indicates that a combination of socio-economic background, ethical awareness, and empathy influences intern doctors’ attitudes toward PLWH. These findings highlight the need for targeted training in medical ethics and empathy to reduce stigma by addressing HIV-related misconceptions and promoting a patient-centred approach. Key messages • Integrating training on confidentiality, anti-discrimination, and accurate HIV knowledge into medical education may reduce stigma, enhance ethical sensitivity, and support respectful care for PLWH. • Raising awareness about the unnecessary nature of additional precautions for PLWH can help correct misconceptions and foster more equitable, empathy-driven healthcare practices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".