Workshop proceedings: values based and culture informed health services and research in circumpolar maternal health
Bibliographic record
Abstract
This paper presents the background, planning, implementation, and outcomes of the inaugural Circumpolar Maternal and Child Health (CMCH) workshop, which aimed to co-develop a values-based and culture-informed research agenda for maternal and child health in the Circumpolar region. Recognizing the historical and ongoing impacts of colonization on Indigenous health systems, the workshop employed community-based participatory research (CBPR) methods to ensure inclusive and equitable engagement of care providers, program designers, Indigenous knowledge holders, and researchers. Through facilitated panels, sharing circles, and breakout sessions, participants explored culturally grounded approaches to maternal health, including the First 1000 Days, trauma-informed care, and birthing practices. Discussions emphasized Indigenous epistemologies, relational approaches, and the need to reframe Western concepts such as risk and trauma through community-defined perspectives. The workshop fostered relationship building and collective reflection, identifying key priorities for future research and collaboration. Reflections underscore the importance of holistic, community-responsive maternal health systems that honour Indigenous values, support local governance, and promote culturally safe, strengths-based care and research.
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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 | 0.051 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".