Exploring the experiences and strategies for promoting the well-being of first-time Black Canadian women transitioning to motherhood: A community-based participatory study protocol
Bibliographic record
Abstract
BACKGROUND: The arrival of a first child is often a transformative and joyful period filled with excitement, but it can also be an overwhelming and isolating experience, especially for first-time mothers facing structural and socio-economic disadvantages. Negative experiences during the early transition to motherhood may contribute to poor maternal mental health and affect the trajectory of infant development. Thus, supporting this journey is critical, yet there is a paucity of research on the experiences and support needs of first-time Black Canadian women transitioning to motherhood. OBJECTIVE: The overall objective of this proposed study is to explore the experiences of transitioning to motherhood and to identify the contextual, sociocultural, and structural conditions that facilitate and or impede healthy transitions among first-time Black Canadian mothers. DESIGN: This proposed study will employ a two-phase critical exploratory qualitative design guided by intersectionality, community cultural wealth, and a socio-ecological framework to explore the perspectives of first-time Black mothers on the contextual, cultural, and structural conditions that facilitate or impede healthy transition to motherhood. METHODS: We will also draw on community-based participatory methods to engage them in translating the findings into accessible and actionable strategies to inform programs, interventions, and policies to support their transition needs. In collaboration with our community partner, six focus groups and 12 individual interviews will be conducted with first-time Black mothers from across Ontario. Data will be analysed using thematic analysis. DISCUSSION: Most research on transition to motherhood has been conducted with white middle-class women. Understanding how Black mothers navigate their transition to motherhood through a complex array of entrenched systemic and racial inequities is critical to inform policy and development of interventions and programs to meet their transition needs including early screening of social and mental health risk factors. CONCLUSION: This project has the potential to inform social policy as it relates to parental leave, workplace accommodation, and accessible childcare to support first-time Black mothers transitioning to motherhood.
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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 | 0.023 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".