Bibliographic record
Abstract
Home healthcare providers play a critical role in the prevention of unintended pregnancies by providing evidence-based contraception education during home visits. This article describes an innovative and comprehensive contraception protocol that was developed for Nurse-Family Partnership to improve contraception education for home healthcare patients. The protocol focused on increasing uptake of long-acting reversible contraception (LARC) for high-risk prenatal and postpartum home healthcare patients. The protocol was designed to reduce early subsequent pregnancies and thereby improve outcomes for mothers and their infants. An evidence-based translation project was designed and piloted in three California counties. The protocol consisted of a contraception education module for nurses and a patient education toolkit. The toolkit included an interactive patient education workbook emphasizing LARC methods for nurses to complete with their patients along with other teaching tools. The project was evaluated using pre- and posttest surveys that measured changes in nurses' knowledge, attitudes, and practice before, after, and 2 months after implementation. Outcomes revealed the following statistically significant results: (a) nurses' knowledge doubled at the first posttest and persisted at 2 months, (b) nurses' attitudes improved on two of the three measures, and (c) there was a 17.7% increase in the frequency of LARC birth control education 2 months after implementation. An evidence-based contraception protocol can promote acceptance of LARC methods and improve home healthcare clinician comfort with and frequency of birth control education.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.087 | 0.023 |
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".