Coping and Suicidal Ideations in Women with Symptoms of Postpartum Depression
Bibliographic record
Abstract
Objective To explore the relationship between coping mechanisms and suicidal ideations among women who experience symptoms of postpartum depression. Design This exploratory descriptive study used secondary data from a study of women who experienced symptoms of postpartum depression. Participants Convenience and purposive sampling were used to obtain the community sample of 40 women who experienced symptoms of postpartum depression. Methods Binary logistic regression was employed to explore emotion-focused coping, avoidance-focused coping, problem-focused coping, and religious coping as predictors of suicidal ideations. Results Approximately 27% of the sample reported suicidal ideations within the past seven days. The results showed that lower levels of emotion-focused coping and higher levels of avoidance-focused and religious coping predicted suicidal ideations in participants. Problem-focused coping did not predict suicidal ideations. Conclusion Overall, our findings provide support for the importance of coping mechanisms as predictors of suicidal ideations among women who experience symptoms of postpartum depression. The results illustrate the need for health professionals to conduct routine assessments on coping strategies and thoughts of suicide when caring for postpartum women, as well as the need to integrate coping approaches in the prevention and treatment of suicidal ideations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".