Kikiskawâwasow - prenatal healthcare provider perceptions of effective care for First Nations women: an ethnographic community-based participatory research study
Bibliographic record
Abstract
BACKGROUND: Pregnant Indigenous women suffer a disproportionate burden of risk and adverse outcomes relative to non-Indigenous women. Although there has been a call for improved prenatal care, examples are scarce. Therefore, we explored the characteristics of effective care with First Nations women from the perspective of prenatal healthcare providers (HCPs). METHODS: We conducted an ethnographic community-based participatory research study in collaboration with a large Cree First Nations community in Alberta, Canada. We carried out semi-structured interviews with 12 prenatal healthcare providers (HCPs) that were recorded, transcribed, and subjected to qualitative content analysis. RESULTS: According to the participants, relationships and trust, cultural understanding, and context-specific care were key features of effective prenatal care and challenge the typical healthcare model. HCPs that are able to foster sincere, non-judgmental, and enjoyable interactions with patients may be more effective in treating pregnant First Nations women, and better able to express empathy and understanding. Ongoing HCP cultural understanding specific to the community served is crucial to trusting relationships, and arises from real experiences and learning from patients over and above relying only on formal cultural sensitivity training. Consequently, HCPs report being better able to adapt a more flexible, all-inclusive, and accessible approach that meets specific needs of patients. CONCLUSIONS: Aligned with the recommendations of the Truth and Reconciliation Commission of Canada, improving prenatal care for First Nations women needs to allow for genuine relationship building with patients, with enhanced and authentic cultural understanding by HCPs, and care approaches tailored to women's needs, culture, and context.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".