Learning in primary health care settings: Australian undergraduate nursing students’ perspectives
Bibliographic record
Abstract
Objective: Primary based health care is increasing. While student clinical placements in primary health care are not new, little is known about what students can learn and there is little evidence around the value they add to overall clinical learning. This study explored undergraduate nursing students’ experiences of learning in two primary health care settings: general practice and Royal District Nursing Service. Design: A qualitative design using semi-structured interviews and focus groups was chosen. Data were analysed thematically. Methods: Nine undergraduate students undertook voluntary one-week placements in each of general practice and district nursing. Following completion of placements, students were interviewed about their placement experiences and focus groups were undertaken with the nurses involved in the study from both the GP setting and District Nursing setting. Results: Three key themes emerged: broadened perspectives, where students’ initial perceptions of primary health nursing were challenged; skills development, with students receiving opportunities to consolidate previously learned skills alongside new skills to be acquired; and contexts of care, enabling students to see the bigger picture of health care delivery, beyond what is delivered in acute settings. Conclusions: Nursing students need to be exposed to expanded settings of care in order to fully appreciate autonomous primary health care nursing roles and gain understandings of how this links to care in the acute care setting. Primary health care based placements are also a vital component in workforce planning exposing careers in this setting as sustainable, viable and highly rewarding.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".