Improving the quality of self-collected swab specimens for the detection of Chlamydia trachomatis and Neisseria gonorrhoeae in a clinical setting
Bibliographic record
Abstract
BACKGROUND: The practice of patient self-collected swab specimens for Neisseria g onorrhoeae and Chlamydia trachomatis is supported in the literature. LOCAL PROBLEM: Health care providers observed that patients sometimes performed their self-swabs incorrectly resulting in cancelled or invalid specimens. METHODS: The clinic's outdated visual aids were replaced with new visual aids. The goal was to improve health care provider proficiency in providing the health teaching and to reduce the clinic's number of cancelled or invalid swab specimens. Staff evaluated the visual aids using an online pretest and post-test survey. The percentage of invalid swabs was calculated before and after project implementation. INTERVENTION: The posters were designed and printed. In-person teaching on the project and using the new visual aids was provided. RESULTS: There was no change in the reported proficiency of staff in providing health teaching for self-collected swab specimens. There was a reduction in staff observed self-swabbing errors. Three percent of rectal swabs were reported as invalid in the 2 weeks before project implementation, and 1.4% of rectal swabs were invalid in the 2 weeks after. CONCLUSIONS: Providing patient health teaching using verbal instructions combined with visual diagrams can improve patients' ability to retain health information.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".