Nurturing maternal health in the midst of difficult life circumstances: a qualitative study of women and providers connected to a community-based perinatal program
Bibliographic record
Abstract
Abstract Background Many socioecological and structural factors affect womenâ s diets, physical activity, and her access and receptivity to perinatal care. We sought to explore womenâ s and providersâ perceptions and experiences of health in the pre- and post-natal period while facing difficult life circumstances, and accessing a community-based program partially funded by Canada Prenatal Nutrition Program (CPNP) in Alberta, Canada. Methods Following the principles of community-based participatory research, we conducted a focused ethnography that involved five focus groups with women (28 in total), eight one-on-one interviews with program providers, and observations of program activities. Data were analyzed through qualitative content analysis to inductively derive codes and categories. Results Women perceived eating healthy foods, taking prenatal vitamins, and being physically active as key health behaviours during pregnancy and postpartum. However, they were commonly coping with many difficult life circumstances, and faced health barriers for themselves and their babies. These barriers included pregnancy or birth complications, family and spousal issues, financial difficulties, and living rurally. On the other hand, women and providers identified many aspects of the community-based program that addressed the burden of adversities as enablers to better health during pregnancy and postpartum. Conclusion Community-based programs have an important role in alleviating some of the burden of coping with difficult life circumstances for women. With such potential, community-based programs need to be well supported through policies. Policies supporting these programs, and ensuring adequate funding, can enable more equitable services to rural women and truly promote maternal health during pregnancy and postpartum.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".