Postpartum Thoughts of Infant-Related Harm and Obsessive-Compulsive Disorder
Bibliographic record
Abstract
Unwanted intrusive thoughts (UITs) of intentional infant-related harm are ubiquitous among new mothers and frequently raise concerns about infant safety. The purpose of this research was to assess the relation of new mothers' UITs of intentional, infant-related harm and obsessive-compulsive disorder (OCD) with maternal aggression toward the infant and to document the prevalence of maternal aggression toward the infant. criteria), and maternal aggression toward the infant. Data for this research were collected from February 9, 2014, to February 14, 2017. Overall, few participants (2.9%; 95% CI, 1.5% to 4.7%) reported behaving aggressively toward their infant. Participants who reported UITs of intentional, infant-related harm (44.4%; 95% CI, 39.2% to 49.7%) were not more likely to report aggression toward their newborn compared with women who did not report this ideation (2.6%; 95% CI, 0.9% to 5.8%; and 3.1%; 95% CI, 1.3% to 6.2%, respectively). The same was true for women with and without OCD (1.9%; 95% CI, 0.3% to 6.4%; and 3.5%; 95% CI, 1.8% to 6.0%), respectively. This study found no evidence that the occurrence of either UITs of intentional, infant-related harm or OCD is associated with an increased risk of infant harm. The prevalence of child abuse of infants in this sample (2.9%) is lower than reported in others (4%-9%). Findings provide critical and reassuring information regarding the relation between new mothers' UITs of intentional harm and risk of physical violence toward the infant.
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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 | 0.006 |
| 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".