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Record W2034318991 · doi:10.1300/j013v37n04_05

Women Teaching Women's Health: Issues in the Establishment of a Clinical Teaching Associate Program for the Well Woman Check

2003· article· en· W2034318991 on OpenAlexaboutno aff
Kathryn Robertson, Kelsey Hegarty, Vivienne O’Connor, Jane Gunn

Bibliographic record

VenueWomen & Health · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationFamily medicinePsychologyMedicineGerontology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.050
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0500.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.443
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2003
Admission routes1
Has abstractyes

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