Identifying and Taking Action on the Protective and Risks Factors of Black Maternal Mental Health: A Community-Based Participatory Research Study Protocol (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Maternal mental health (MMH) disorders are associated with adverse maternal and infant health outcomes. Despite advances in screening and treatment, disparities in MMH disorders continue to disproportionately affect Black mothers and birthing persons. In Canada, there are gaps in research on the protective and risks factors of Black MMH, hindering the advancement of inclusive policies and practices to promote maternal well-being and optimal outcomes for Black mothers/ birthing persons and their children. The objective of this proposed study is to identify the protective and risks factors of Black maternal mental health and engage Black mothers/birthing persons in co-designing a culturally safe and inclusive best practices model to inform policy and programming. The proposed study will use an exploratory 3-phase sequential mixed methods approach guided by principles of community-based participatory research to survey 300 participants. Five focus groups/individual interviews along with group concept mapping will be used to examine the sociocultural, contextual, and structural determinants of Black MMH and identify areas for policy action. The proposed project is well-positioned to shift policy, practice, and research, and support capacity building among Black mothers/birthing persons. The research results will be used to advocate for policy interventions and initiatives at the health system and community level and build capacity among service providers to provide culturally safe and equitable mental health care for Black mothers and birthing persons.
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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 | 0.090 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
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".