Midwifery Trained Registered Nurses' Perceptions of Their Role in the Labor Unit
Bibliographic record
Abstract
Introduction: A Midwifery Trained Registered Nurse (MTRN) is a member of the multi-professional maternity health care team in Sri Lanka. Her contribution to the maternity care team is poorly understood, often undermined, and undefined. In the context of low- and middle-income settings where traditional midwives play a crucial role in domiciliary care, the MTRNs role as a member of the multi-professional hospital-based maternity care team has not been well-described. Objective: The study aimed to describe MTRNs' perceptions of their role in the Labor Unit within the multi-professional maternity health care team at five tertiary care hospitals in the Capitol Province of Sri Lanka. Materials and Methods: A descriptive cross-sectional study was conducted among 186 MTRNs working in labor rooms in the study setting. All MTRNs in the selected hospitals were invited and included in the sample. A postal survey was carried out using a pre-evaluated, pretested self-administered questionnaire, and descriptive statistics were derived. Results: All respondents were females, aged 27 to 60 years (mean ±SD 40 ±8.3 years). The majority (66%)was less than 45 years old. Almost all (>96%) MTRNs perceived 12 tasks of the listed tasks as their primary responsibility. Regarding other tasks, they perceived a high degree of overlap between their role and those of the doctors and midwives. Although almost all MTRNs rated the level of interprofessional collaboration from registered nurses (RNs) and doctors as average to good, nearly half (49%) of them rated support from midwives ranging from very poor to average. Conclusion: A high degree of perceived overlap between MTRNs' tasks with those of the other members of the maternity care team can cause role confusion, conflicts, and poor patient care. MTRNs' role in the Labor Unit within the multi-professional maternity health care team was controversial. Clarifying the MTRNs scope of practice will help improve interprofessional understanding of roles and responsibilities and collaboration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".