Orchestrating care for a good life event: a hermeneutic study of the overlooked practices of rural perinatal nurses
Bibliographic record
Abstract
BACKGROUND: Sustaining rural perinatal services is increasingly difficult, largely due to ongoing challenges in recruiting and retaining healthcare providers, including registered nurses. In Canada, where most births take place in hospitals, nurses play central roles in rural prenatal, intrapartum, and postpartum care. Through articulating nurses' everyday practices, their considerable contributions to woman-centred care can be made visible, their value clarified, and appropriate supports to retain them identified. The aim is to provide an in-depth understanding of what it means to be a nurse providing perinatal care in a rural hospital. METHODS: This hermeneutic study was conducted in partnership between perinatal nurses and university-based researchers. In-depth interviews with 26 nurses, physicians, other professionals, and birthing women along with shadowing nurses as they went about their practice provided data for a multi-stage hermeneutic interpretation. RESULTS: We identified five facets of nurses' practice: providing individualized prenatal care, orchestrating care in the birthing centre, working with and through the team, maintaining the flow of the unit and the care of birthing women and being a generalist in a specialized area. Nurses were seen to play critical but often overlooked roles in providing woman-centred care to pregnant and birthing women, their newborns, and families. Nurses continuously attended to the social, emotional, and physical health of those in their care. Their practice was attuned to the unexpected in the context of the individual woman, interprofessional team, available resources, isolated geography and weather. They showed often hidden and taken-for-granted perinatal knowledge and skills, including knowledgeable anticipation, problem-solving, and making interprofessional teams and systems work. CONCLUSIONS: Through providing finely tuned individual care and orchestrating care, the nurses facilitate birthing as a good life event for childbearing families, even in times of difficulty and uncertainty. As rural perinatal services continue to be at risk of closure, uncovering and articulating nurses' everyday practice and supportive mechanisms create the potential to highlight nurses' value and garner greater respect for their practice and contributions to care and the sustainability of rural perinatal services.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".