Describing how Expert Labour and Delivery Registered Nurses Work to Optimize Outcomes for Intrapartum Patients at Risk of Unplanned Caesarean Section
Bibliographic record
Abstract
Unplanned caesarean section (c-section) births are common and can have significant negative psychological and emotional outcomes for patients. Expert labour and delivery registered nurses (L&D RNs) are well-positioned to anticipate the need for an unplanned c-section and to support at-risk patients. This qualitative interpretive descriptive study explored how expert L&D RNs anticipate unplanned c-sections and support those patients they deem to be at imminent risk of requiring the procedure. A purposive sample of 16 L&D RNs with minimum five years of L&D nursing experience in Ontario participated in semi-structured telephone interviews. Conventional content analysis was used to analyze interview data. Findings describe tacit knowledge used to anticipate unplanned c-sections, and therapeutic nurse-patient relationships developed to individualize support. The findings bring attention to tacit knowledge that expert L&D RNs use in practice and inform future research into the effectiveness of how expert nurses’ interventions support patients at risk of unplanned c-section.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".