British Columbia Healthy Connections Project process evaluation: a mixed methods protocol to describe the implementation and delivery of the Nurse-Family Partnership in Canada
Bibliographic record
Abstract
BACKGROUND: The Nurse-Family Partnership is a home visitation program for first-time, socially and economically disadvantaged mothers. The effectiveness of this public health intervention has been well established in the United States; however, whether the same beneficial outcomes will be obtained within the Canadian context is unknown. As part of the British Columbia Healthy Connections Project, which includes a trial comparing Nurse-Family Partnership's effectiveness with existing services in British Columbia, we are conducting a process evaluation to describe and explain how the intervention is implemented and delivered across five regional Health Authorities. METHODS: A convergent parallel mixed methods research design will be used to address the process evaluation objectives. The principles of interpretive description will guide all sampling, data collection and analytic decisions in the qualitative component of the study. The full population of public health nurses and supervisors (n = 71) will discuss their experiences of implementing and delivering the program in interviews (or focus groups). Managers (n = 5-15) responsible for this portfolio will also be interviewed annually. Fidelity reports with quantitative data on the reach and the dose of the intervention will be collected and analyzed. Summaries of team meetings and supervisory sessions will be analyzed. Data will be used to compare, corroborate and explain results and variances across the five regional Health Authorities. DISCUSSION: The process evaluation results will be of immediate instrumental use to the program implementers to inform intervention delivery. Findings will contribute to the emerging body of evidence surrounding: 1) professional nurse home visitation practice issues; 2) best practices for meeting the needs of families living in rural and remote communities; 3) a deeper understanding of how health and social issues such as mental health problems including substance misuse and exposure to intimate partner violence affect a young mother's capacity to parent; and 4) strategies to support professionals from the primary care, public health and child welfare sectors to work collaboratively to meet the needs of children and families who are at risk or experiencing maltreatment.
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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 | 0.160 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".