Midwives perceptions of managing pregnancies complicated by obesity: A mixed methods study
Bibliographic record
Abstract
OBJECTIVE: The growing prevalence of obesity is a concern for midwives. In Canada, the absence of regulatory standards, varying protocols and consultant preferences shape clinical decision making for the midwife and may lead to inconsistent practice. Our aim was to understand the barriers, enablers, and knowledge gaps that influenced experiences of midwives in Ontario, Canada when providing care to clients impacted by obesity. METHODS: Mixed methods design using a sequential, explanatory approach. Surveys conducted with midwives were administered using an online platform, followed by semi-structured interviews to understand the perspectives elicited in the survey in greater detail. Interviews were audio recorded and transcribed verbatim. Survey data were analyzed using descriptive statistics, and thematic analysis was used for generating codes, categories and themes from the interview data. RESULTS: 144 midwives completed the survey and 20 participated in an interview. The participants described their clinical management when caring for those with obesity which included considerations regarding additional tests/investigations, consultation and transfer of care, and place of birth. Up to 93% of surveyed midwives believed that clients with obesity were appropriate for midwifery-led care however there was less certainty about suitability as BMI increased to higher ranges such as > 45). The care management was influenced by beliefs and attitudes, knowledge, and system-level factors. Midwives experienced barriers such as inconsistent practices and role confusion, and felt ill equipped to care for pregnancies affected by obesity due to unclear guidelines. CONCLUSIONS: Overall, midwives believe clients with obesity are suitable for midwifery-led care due to its individualized, non-judgmental approach to care. Additional training for midwives and other obstetric care providers would be beneficial to help overcome barriers in providing effective care to pregnancies affected by obesity.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".