Social pediatrics in a baccalaureate nursing curriculum
Bibliographic record
Abstract
This paper describes the use of social pediatrics in one baccalaureate nursing curriculum. Social pediatrics is a conceptual model that considers health as physical health and the social determinants of health. Social pediatrics focuses on community-based primary healthcare services for at-risk children and their families. The social pediatrics model is used by community early childhood education StrongStart sites in one Canadian province; these sites are collaborations between early childhood educators and public health nursing teams for children from infancy through five years of age. Acute care clinical placements are becoming too complex and limited in number to accommodate large undergraduate nursing cohorts. Our undergraduate nursing program recently shifted acute care pediatric placements to StrongStart sites, combining community pediatric and public health nursing learning objectives and learning activities that foreground social pediatrics. The acute care component of pediatric nursing includes classroom theory, clinical laboratory and virtual simulations. This paper describes social pediatrics integration within our undergraduate curriculum between 2018-2019; and a qualitative evaluation of our social pediatrics approach in 2019-2020. We used content analysis to identify common themes from interviews with key actors, including students’ clinical instructors, StrongStart sites’ early childhood educators and managers, and public health nurse managers affiliated with StrongStart sites. Common themes were related to social pediatrics learning opportunities and drawbacks; social pediatrics knowledge, skills and attitudes; and recommendations for curriculum enhancement.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".