Thoughts of self-harm and associated risk factors among postpartum women in Canada
Bibliographic record
Abstract
BACKGROUND: The prevalence of thoughts of self-harm during the postpartum period is not well documented in Canada. To estimate the prevalence of thoughts of self-harm among postpartum women in Canada, this study explored prevalence by socio-demographic characteristics and examined the associations between thoughts of self-harm and aspects of maternal mental health. METHODS: This study used data from the 2018/2019 Survey on Maternal Health which surveyed women living in the 10 provinces anywhere between 6-13 months postpartum. Participants were asked to report experiencing thoughts of self-harm, rate their mental health, and participate in the abbreviated Edinburgh Postpartum Depression Scale and Generalized Anxiety Disorder (GAD) scale. Adjusted logistic regression analyses were performed to examine associations. RESULTS: Of the 6,558 respondents who agreed to share their data, 10.4% reported thoughts of self-harm since the birth of their child. Of these women, 37.0% reported low mental health, 54.2% had moderate levels of symptoms of postpartum depression (PPD) and 37.1% had symptoms of GAD. Women who experienced low mental health, moderate levels of symptoms of PPD and/or GAD were more likely to report thoughts of self-harm. LIMITATIONS: As thoughts of self-harm and aspects of mental health are self-reported, there is the potential for social desirability bias and underreporting. The cross-sectional survey design did not allow the reporting of thoughts of self-harm at different time points. DISCUSSION: The high proportion of postpartum women in Canada reporting thoughts of self-harm and strong associations with aspects of maternal mental health highlight the need for effective supports during postpartum.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".