Qualitative Study of Experiences with an Interprofessional Perinatal Care Team Among Women Who Used Substances During the Perinatal Period
Bibliographic record
Abstract
OBJECTIVE: To explore how women who used substances during the perinatal period perceived the care they received from interprofessional perinatal care providers. DESIGN: Appreciative inquiry. SETTING: Interprofessional perinatal care clinic in a large urban tertiary care hospital in Canada. PARTICIPANTS: Fourteen women with various backgrounds who used substances during pregnancy, including opioids, marijuana, and/or methamphetamine, and engaged in care with an interprofessional perinatal care team. The participants identified as First Nations (n = 3), Métis (n = 8), and White (n = 3). METHODS: Using appreciative inquiry, we followed the 4-D cycle of discovery, dream, design, and destiny to frame the semistructured interviews and analyze the data. RESULTS: Four overarching themes with nine subthemes emerged, representing participants' experiences with the interprofessional perinatal care team. The overarching themes were Safe Care, Compassionate Care, Dignified Care, and Connected Care. Participants suggested opportunities to improve care in relation to integration of cultural care, coordination of postpartum services, and increased support in the birth and hospital setting. CONCLUSION: The findings highlight the strengths and assets of interprofessional perinatal care from the patients' perspectives. Participants outlined actionable ways for all perinatal providers to deliver safe, compassionate, dignified, and connected care, which can result in life-giving and lifesaving outcomes for patients.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| 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".