Women's autonomy for managing labour pain in a relational context: An interpretive description study
Bibliographic record
Abstract
AIM: To describe how women perceived relational autonomy for decision-making during childbirth pain and illuminate influencing factors. BACKGROUND: Most women report challenging pain during birth. Circumstances can affect their ability to engage in pain management decisions. DESIGN: We used an interpretative description approach to conduct this study. METHOD: A purposive sample of ten women who reported pain during childbirth participated in semi-structured interviews. The study was conducted between July 2019 and November 2020 and reported according to the COREQ checklist. RESULTS: Circumstances during childbirth, such as women's expectations and relationships, influenced their efforts to engage in relational autonomy. Care providers dealt with the unpredictability of childbirth and challenges with pain management using decision-making practices that could disrupt women's expectations, undermine women's trust, demonstrate disrespect for women and rely on inadequate communication. Women who felt dependent on others were less likely to participate in decision-making. When care providers' perceptions about pain differed from women's reports of pain, participants became distressed because care providers did not acknowledge their subjective pain experiences. CONCLUSIONS: Women regarded their relationships and communication with care providers as foundational to relational autonomy in decision-making about pain management during childbirth. RELEVANCE TO CLINICAL PRACTICE: Study findings can support care providers' considerations of the complexity of childbirth pain and factors affecting women's relational autonomy in decision-making about pain. In particular, the findings highlight the importance of women's expectations and care providers' recognition of women's experiences of pain. PATIENT OR PUBLIC CONTRIBUTION: Women who shared their stories of childbirth pain contributed to the data collected. The chief nursing officers in the data collection setting facilitated the recruitment and data collection.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".