Understanding young people’s barriers to sexually transmitted disease screening and meeting their needs: A focus group study
Bibliographic record
Abstract
Background: Chlamydia is the most common bacterial sexually transmitted disease (STD) in adolescents, and may have severe consequences such as pelvic infections, cervical cancer and infertility if left untreated. Studies have determined that young people have several barriers towards getting tested for STD. This study seeks not only to address the barriers to STD testing but also to understand them and thereby improve the quality of care in an outpatient clinic and increase screening rates. Methods: The study uses a phenomenological, hermeneutical approach through qualitative interviews. Four focus groups with a total of 22 participants in the age between 16-25 years were conducted for this study. Results: It was found that testing for STD is still associated with stigmatization and our findings indicate that testing for STD should undergo normalization. At the same time health care providers face a major challenge in advising young people due to the lack of knowledge and the fear of being reprimanded. Conclusions: None of the participants were aware of our clinic. Changes were made in the outpatient clinic to consider the needs of the young people. We implemented non-invasive screening methods for those attending asymptomatic screening, introduced both nurse-led consultations and anonymous counseling regarding STDs and testing.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".