Development of patient-centered outcomes for labour and birth: a qualitative study
Bibliographic record
Abstract
Background: Current quality improvement models in obstetrics focus on prevention of adverse perinatal outcomes. The development of these metrics was based on expert opinion that did not account for patients’ values. The ultimate aim of our research is to develop performance indicators for labour and birth that reflect the patient perspective. Methods: A qualitative interview design was used to engage a convenience sample, of recent (<1 year) postpartum patients, in semi-structured interviews, where they shared their experiences of their recent birth. Patients were also asked to assess descriptions of adverse perinatal outcomes for readability and comprehension, towards developing accurate unbiased descriptions for a subsequent survey of patients to weight complications. Responses were recorded, transcribed, coded and analyzed using thematic analysis. thematic analysis. Results: Five themes emerged during the analysis: (1) desire for patient-centred care, (2) improved communication, (3) labour/birth, expectations and outcomes, (4) care team support during labour and birth, (5) continuing emotional and physical postpartum care. Conclusions: Patient-centred care and good health outcomes were the major values expressed by the patients in this study. Good communication and shared decision making led to patients describing their labour and birth as a satisfying experience. This study lays the foundation for developing a quality tool to measure the outcomes of birth and adverse outcomes from the patients’ perspective.
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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 | 0.045 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".