Mixed-methods study exploring health service access and social support linkage to the mental well-being of Canadian Indigenous pregnant persons during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVES: This study aimed to explore how the unprecedented stressors associated with the COVID-19 pandemic may have contributed to heightened levels of depression and anxiety among pregnant Indigenous persons, and identify protective individual-level factors. DESIGN: The current study used a mixed-methods design including standardised questionnaires and open-ended response questions. Using hierarchical regression models, we examined the extent to which COVID-19-related factors of service disruption (ie, changes to prenatal care, changes to birth plans and social support) were associated with mental well-being. Further, through qualitative analyses of open-ended questions, we examined the coping strategies used by pregnant Indigenous persons in response to the pandemic. SETTING: Participants responded to an online questionnaire consisting of standardised measures from 2020 to 2021. PARTICIPANTS: The study included 336 self-identifying Indigenous pregnant persons in Canada. RESULTS: Descriptive results revealed elevated rates of clinically relevant depression (52.7%) and anxiety (62.5%) symptoms among this population. 76.8% of participants reported prenatal care service disruptions, including appointment cancellations. Thematic analyses identified coping themes of staying informed, social and/or cultural connections and activities, and internal mental well-being strategies. Disruptions to services and decreased quality of prenatal care negatively impacted mental well-being of Indigenous pregnant persons during the COVID-19 pandemic. CONCLUSIONS: Given the potential for mental well-being challenges to persist and long-term effects of perinatal distress, it is important to examine the quality of care that pregnant individuals receive. Service providers should advance policies and practices that promote relationship quality and health system engagement as key factors linked to well-being during the perinatal period for Indigenous 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".