Nursing role in well-child care: Systematic review of the literature.
Bibliographic record
Abstract
OBJECTIVE: To describe and compare well-child care (WCC) in Australia, the Netherlands, and the United Kingdom (UK), focusing on the role of nurses and their interactions with other primary care providers in order to derive relevant lessons for Canada's interprofessional primary care teams. DATA SOURCES: and other synonymous terms. In addition, Google Scholar was used to search for gray literature, and reference mining revealed a few other relevant articles. STUDY SELECTION: The original search identified 929 articles. The inclusion criteria were the following: relevant to WCC delivery; focuses on Canada, the Netherlands, the UK, Australia, or an international comparison; describes care of healthy term infants; describes care provided in the community; and describes the role of the nurse in WCC delivery. An abstract review followed by full-text review condensed the search to 25 selected articles. SYNTHESIS: Selected articles varied in method and scope; thus, a narrative synthesis was generated using thematic analysis. In Australia, the Netherlands, and the UK, many WCC tasks are performed by trained public health nurses in a separate but parallel system to family medicine, with interaction between nurses and FPs varying greatly among countries. In general, nurses' roles in WCC remained in the preventive care and screening domains, including monitoring development, providing health education, and supporting parents. The 3 overarching themes that were identified were around professional development and education, integration of care and interprofessional collaboration, and the nurses' role in an evolving health system. CONCLUSION: International examples, given Canada's primary care reforms, suggest it is time to examine greater role sharing in WCC between nurses and FPs in interdisciplinary primary care teams.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".