Recreating the village: the patient experience with a hybrid model of Group Perinatal Care (GPPC) in an academic family health team
Bibliographic record
Abstract
BACKGROUND: Group prenatal care (GPC) has been shown to have a positive impact on social support, patient knowledge and preparedness for birth. We developed an interprofessional hybrid model of care whereby the group perinatal care (GPPC) component was co-facilitated by midwives (MW) and family medicine residents (FMR) and alternating individual visits were provided by family physicians (FP's) within our academic family health team (FHT) In this qualitative study, we sought to explore the impact of this program and how it supports patients through pregnancy and the early newborn period. METHODS: Qualitative study that was conducted using semi-structured telephone interviews with 18 participants who had completed GPPC in the Mount Sinai Academic Family Health Team in Toronto, Canada and delivered between November 2016 and October 2018. Interviews were audio-recorded and transcribed verbatim. Thematic analysis was conducted by team members using grounded theory. RESULTS: Four over-arching themes emerged from the data: (i) Participants highly valued information they received from multiple trusted sources, (ii) Participants felt well cared for by the collaborative and coordinated interprofessional team, (iii) The design of GPPC enabled a shared experience, allowing for increased support of the pregnant person, and (iv) GPPC facilitated a supportive transition into the community which positively impacted participants' emotional well- being. CONCLUSIONS: The four constructs of social support (emotional, informational, instrumental and appraisal) were central to the value that participants found in GPPC. This support from the team of healthcare providers, peers and partners had a positive impact on participants' mental health and helped them face the challenges of their transition to parenthood.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".