Improving the Nurse–Family Partnership in Community Practice
Bibliographic record
Abstract
BACKGROUND: Evidence-based preventive interventions are rarely final products. They have reached a stage of development that warrant public investment but require additional research and development to strengthen their effects. The Nurse-Family Partnership (NFP), a program of nurse home visiting, is grounded in findings from replicated randomized controlled trials. OBJECTIVE: Evidence-based programs require replication in accordance with the models tested in the original randomized controlled trials in order to achieve impacts comparable to those found in those trials, and yet they must be changed in order to improve their impacts, given that interventions require continuous improvement. This article provides a framework and illustrations of work our team members have developed to address this tension. METHODS: Because the NFP is delivered in communities outside of research contexts, we used quantitative and qualitative research to identify challenges with the NFP program model and its implementation, as well as promising approaches for addressing them. RESULTS: We describe a framework used to address these issues and illustrate its use in improving nurses' skills in retaining participants, reducing closely spaced subsequent pregnancies, responding to intimate partner violence, observing and promoting caregivers' care of their children, addressing parents' mental health problems, classifying families' risks and strengths as a guide for program implementation, and collaborating with indigenous health organizations to adapt and evaluate the program for their populations. We identify common challenges encountered in conducting research in practice settings and translating findings from these studies into ongoing program implementation. CONCLUSIONS: The conduct of research focused on quality improvement, model improvement, and implementation in NFP practice settings is challenging, but feasible, and holds promise for improving the impact of the NFP.
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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 | 0.051 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".