Bibliographic record
Abstract
Background Partner notification (PN) is an important public health activity in STI control to stop onwward transmission. Various forms of PN services have been developed but not all have been evaluated to the same extent. In the era of evidence-based resource allocation, it is of utmost importance to focus limited resources on services shown to be the most efficient and effective. Methods A review of the current literature and of the National Collaborating Centre for Infectious Diseases (NCCID) STBBI partner notification (PN) project productions was conducted. The impact of these various forms of PN services on disease incidence, re-infection, relationship status and healthcare costs will serve as efficiency and effectiveness markers. Results Outcomes of MSM PN services has been measured and found to be associated with reduced index case GC and CT reinfection rates through patient delivered therapy, higher adoption of safer sexual practises in both index case and their partners, reduced incidence of STIs, higher rates of notification to long term partners and significant partners, high acceptability of face-to-face patient delivered partner notification in significant or long term relationships compared to higher acceptability of physician or electronic notification for casual or anonymous partnerships lower cost per case reached by patient referral compared to provider referral, lower levels of stress in relationships. Emotional and physical abuse after PN services can occur. The fears accompanying PN services can affect sexual spontaneity. Caution should be used before discarding PN services when efficiency or effectiveness is low because epidemiologic insight can still be gathered to help redirect screening activities. Conclusions A Review of the evidence indicates that MSM PN services works!
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.007 |
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".