Women Teaching Women's Health: Issues in the Establishment of a Clinical Teaching Associate Program for the Well Woman Check
Bibliographic record
Abstract
The impact of screening programs for cervical cancer would be increased with the greater participation of currently underscreened women. Training for medical students and doctors in the fine technical and communication skills required in breast and gynaecological examinations would improve participation by increasing the confidence and skill of doctors in raising the issue of screening, thereby making the examination a more positive experience for women. Gynaecology Teaching Associate (GTA) programs, using specially trained standardized patients, have been used in over 90% of American and Canadian medical schools for more than ten years to provide such training. Australia has been slow to adopt this teaching method. A Clinical Teaching Associates in Gynaecology program (CTA) was first established in 1996 by the Department of Obstetrics and Gynaecology at the University of Queensland, building on the Pap test program from Adelaide. Other medical schools subsequently introduced such programs and in 2000, the Department of General Practice, University of Melbourne, established a CTA program based on the Queensland program, with a grant from PapScreen Victoria. This paper describes the methods of recruitment and training of CTAs, use of CTAs in the medical course, preliminary evaluation, and ethical and other issues in the Melbourne and Queensland University programs.
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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 | 0.053 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".