Single Room Maternity Care Model: Unit Culture and Healthcare Team Practices
Bibliographic record
Abstract
The evidence regarding the effects of a Single Room Maternity Care (SRMC) model on women’s childbirth experiences, healthcare providers’ workplace satisfaction, and cost outcomes remains equivocal. The research questions for this focused ethnographic study are: how is culture experienced by nurses and other healthcare providers on the SRMC unit, and how do the values, beliefs, and norms of nurses and other healthcare providers on the SRMC unit influence their day-to-day practices of caring for women and their families. The aim of this qualitative focused ethnography was to explore the culture and practices of the healthcare team in a SRMC unit. Twelve healthcare providers were recruited from a Single Room Maternity Care unit located in a Western Canadian hospital. Semi-structured interviews, participant observations, and examination of unit-related documents were conducted between October 2014 and January 2015. Data were analyzed using an approach by Roper and Shapira (2000). Two main themes emerged from the data: creating and maintaining culture and the work family. The participants considered themselves a family, and made collective and conscious efforts to create a unit culture where everyone could feel supported and valued. Unit culture determined the ways members of the healthcare team functioned in their day-to-day practice. Further research is required to explore the relationship between the maternity unit and quality of patient care, as well as the impact of collaborative practices on both providers and recipients of maternal care.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".