P047 The three Rs: recalls, reminders and retesting for chlamydia – views of GPs and young adults
Bibliographic record
Abstract
<h3>Background</h3> Chlamydia reinfection increases the risk of pelvic inflammatory disease. Reinfection is common in Australia and while clinical guidelines recommend retesting 3 months post-treatment, less than 25% are retested. We aimed to examine general practitioner (GP) and patient views on retesting for chlamydia and recall/reminder systems to facilitate retesting. <h3>Methods</h3> As part of a trial of chlamydia testing in general practice, GPs were provided with resources and support to implement recall/reminder systems to increase retesting. GPs’ attitudes and practices were examined pre- and mid-intervention using semi-structured interviews. Semi-structured interviews were also conducted with patients throughout the trial. Data were analysed thematically. <h3>Results</h3> 44 GPs undertook a pre-intervention and 24 a mid-intervention interview; 22 patients were interviewed. GPs viewed recalls/reminders as essential to a formal chlamydia control program. There was disparity in whether systems to enable retesting were adopted during the intervention. Barriers to implementing these systems included concerns about costs and time required to ‘chase up’ patients; these barriers persisted during the intervention. Concerns about privacy were raised by most GPs but not patients. Over half of patients were not provided with advice about retesting at the time of their initial chlamydia test. Of the four patients who tested positive, two were retested as per guidelines. Patients were universally supportive of receiving reminders for chlamydia retesting, though retesting when at the clinic for another reason was viewed as ‘more practical’. Patients did not have strong preferences about reminder type (letter, SMS, email). Knowledge gaps were identified by both GPs and patients, and GPs identified a need to improve knowledge of the risks of chlamydia reinfection. <h3>Conclusion</h3> GPs raised more concerns about retesting and reminders than patients. Increasing GP and patient knowledge of the risks of reinfection is crucial. GPs require additional support to implement strategies to increase re-testing. <h3>Disclosure</h3> No significant relationships.
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 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".