Pap smear collection: Proposal of a low-cost simulator for health education
Bibliographic record
Abstract
Objective: To develop a low-fidelity and low-cost simulator for health education in Pap smears.Methods: This is an experience report of the care practice carried out with nine nurses and twenty-five women, during Pap smears in the office of the Rural Zone Family Health Team. The SQUIRE 2.0 method was used in the following steps Context: the issue was analyzed. Intervention: searches for the existence of a simulator with the same purpose. The simulator was built manually, using low-cost materials. Measurements: an evaluation of the nurses' perception of the technology produced was performed. Afterward, it was used with women undergoing Pap smears. Analysis: evaluation was carried out by means of simple descriptive statistics.Results: A low-cost and fidelity simulator formed by six slides, where the first slide represents a normal cervix; followed by a slide representing a cervix with cervicitis; a cervix with polyp; a cervix with a lesion in the transformation zone; a cervix with malignant neoplasm; and, by a figure equal to the first slide, however, the hole of the endocervix is perforated in order to introduce the brush of the Pap smear kit.Conclusion and implications for practice: A strengthening of cervical cancer preventive strategies in Primary Health Care was obtained. The simulator allows visualization of the main components of the female genital organ when introducing the speculum, configuring a creative and innovative health education strategy for nursing practice.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".