Prevalence and Course of Unwanted, Intrusive Thoughts of Infant-Related Harm
Bibliographic record
Abstract
Unwanted, intrusive thoughts (UITs) of infant-related harm are a common postpartum phenomenon and can be classified into thoughts of accidental harm (TAHs) and thoughts of intentional harm (TIHs). Our study's objective was to complete a comprehensive, comparative analysis of TAHs and TIHs by commenting on their prevalence, course, characteristics (time, distress, and impairment) and most intense period. A total of 763 English-speaking pregnant women across British Columbia were recruited to participate in a prospective cohort study. Study data were collected between February 2014 and February 2017. UITs were assessed by semistructured interviews twice during the postpartum period. The prevalence of TAHs and TIHs in the postpartum period was 95.8% and 53.9%, respectively. The most common TAHs included thoughts of the baby suffocating or dying from sudden infant death syndrome; the most common TIHs included thoughts of neglect. On average, TAHs are more prevalent, time-consuming, and result in greater interference compared to TIHs. The most intense period for TAHs (5.74 weeks postpartum) and TIHs (within first 8 weeks postpartum) was identified. During this period, over 40% of participants reported moderate or extreme distress related to UITs. For most, UITs decreased in frequency or completely resolved by 6 months postpartum, and most participants did not report clinically significant symptoms. UITs are a normative and typically self-resolving occurrence in the postpartum period. UITs' most intense period signifies a time of heightened vulnerability. Increased education is necessary to normalize and reduce distress associated with UITs. .
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".