Exploring the Contributions of Nursing to Well-Child Care within Interprofessional Primary Care Teams
Bibliographic record
Abstract
Background Registered nurses (RN) are well positioned to deliver well-child care, and health system reform calls for the effective use of interprofessional health care providers to meet the evolving needs within the primary care sector. Despite this, little is known about how RNs contribute to the delivery of well-child care or the attributes of the nursing care organization that influence RN scope of practice enactment in the delivery of well-child care. Purpose The overarching aim of this study was to explore attributes within the nursing care organization framework related to RN contributions to well-child care within the context of interprofessional primary care teams. Methodology To address the research aim, a multiple methods study was conducted which included a scoping review and a multiple-case study. We conducted the scoping review using the Joanna Briggs Institute (JBI) scoping review methodology. We used Yin’s approach to case study methodology for the multiple-case study. Cases included three interprofessional primary care teams in Ontario. Data collected included surveys, interviews, and administrative electronic health data. Descriptive pattern matching was the analytic approach implemented. Findings Dimensions of well-child care were well aligned to the scope of practice of RNs; however, scope of practice enactment varied between and within organizations. Support for the RN role, interprofessional collaboration, trust, role clarity, strategic leadership, funding structures, and team composition influenced how RNs contributed to the delivery of well-child care. Conclusions The findings from this study identify how attributes of the nursing care organization influence scope of practice enactment and delivery of well-child care. In particular, findings will support the optimization of the nursing role in primary care and enhance access to well-child care among those who currently do not have access.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".