‘It Opened My Eyes’—examining the impact of a multifaceted chlamydia testing intervention on general practitioners using Normalization Process Theory
Bibliographic record
Abstract
Background: Chlamydia is the most common notifiable sexually transmissible infection in Australia. Left untreated, it can develop into pelvic inflammatory disease and infertility. The majority of notifications come from general practice and it is ideally situated to test young Australians. Objectives: The Australian Chlamydia Control Effectiveness Pilot (ACCEPt) was a multifaceted intervention that aimed to reduce chlamydia prevalence by increasing testing in 16- to 29-year-olds attending general practice. GPs were interviewed to describe the effectiveness of the ACCEPt intervention in integrating chlamydia testing into routine practice using Normalization Process Theory (NPT). Methods: GPs were purposively selected based on age, gender, geographic location and size of practice at baseline and midpoint. Interview data were analysed regarding the intervention components and results were interpreted using NPT. Results: A total of 44 GPs at baseline and 24 at midpoint were interviewed. Most GPs reported offering a test based on age at midpoint versus offering a test based on symptoms or patient request at baseline. Quarterly feedback was the most significant ACCEPt component for facilitating a chlamydia test. Conclusions: The ACCEPt intervention has been able to moderately normalize chlamydia testing among GPs, although the components had varying levels of effectiveness. NPT can demonstrate the effective implementation of an intervention in general practice and has been valuable in understanding which components are essential and which components can be improved upon.
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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 | 0.031 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".