How is primary care nursing embedded in nursing undergraduate curricula: A mixed‐method study from four countries
Bibliographic record
Abstract
Abstract Introduction Given the increase in the primary care nursing workforce and the need to further grow nursing roles in the community understanding how this is included in undergraduate education is important. This study aimed to explore the inclusion of primary care in the undergraduate nursing education curriculum of four countries. Method A mixed‐method design was employed to obtain a broader context of primary care nursing in nursing education and teaching approaches relating to primary care nursing. Subsequently, Australia, Canada, Spain, and Ireland were selected for this study because primary healthcare systems are established as a part of their healthcare system. Results In total, 136 nursing faculties (40 in Australia, 35 in Canada, 46 in Spain, and 15 in Ireland) were invited to participate in this study. Of these, 27 responses were obtained (19.8% response rate). Following the survey phase, in‐depth interviews were conducted with 13 participants. The results indicated that the highest number was coded within the theme of “Understanding of PHC in the curriculum” (n = 108). The second highest number (n = 87) was within the theme of “Interpretation differences of PHC in curriculum,” and the third highest (n = 31) was coded within “Policy impact on health by national government and others.” Conclusion The results emphasized the ambiguity of primary care within the undergraduate nursing curriculum and that interpretation and implementation into the curriculum largely depended on the school's intentions.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".