Well Woman's Group Medical Appointment: For screening and preventive care.
Bibliographic record
Abstract
PROBLEM ADDRESSED: Maintaining a screening program, such as regular Papanicolaou testing, can be a challenge for primary care practices on account of long wait times and patient factors. OBJECTIVE OF PROGRAM: To effectively and efficiently improve access to appointments and to provide screening and patient education in a socially supported setting. PROGRAM DESCRIPTION: A group medical appointment called the Well Woman's Group Medical Appointment has been developed that focuses on Pap smear preventive screening tests. Women are invited by their family physicians, and group appointments are booked for 2 hours on a day when the whole office can be used. Each woman is given a "Pap bag" (containing a labeled slide in its protective case, a spatula, a cytobrush, and patient labels) and sent to a waiting room stocked with healthy snacks, tea, coffee, and pens. While each woman is getting tested, the others have a chance to ask questions. Each woman's height, weight, blood pressure, date of last period, and body mass index are recorded. At the end of the session, the women fill out an evaluation form. Afterward there is a short debriefing session, all information is transferred to each patient's electronic medical record, and the slides are sent to the laboratory for testing. CONCLUSION: This program is a viable way to improve office processes and an efficient way to complete women's screening tests, meet goals to improve women's health care, and shorten waiting lists. The increased role of the medical office assistant improves outcomes, and group appointments can be applied to a number of health care measures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.314 | 0.098 |
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".