fMRI and Endocrinological Studies of Depression and Anxiety Following the Birth or Adoption of a Child: Towards a Model of Feminist Science
Bibliographic record
Abstract
This paper reviews selected literature between 2000 and 2015 on efforts, through fMRIs and endocrinological studies, to ascertain the causes of depression and anxiety following the birth or adoption of a child and to improve treatment. Typically, only the brains of postpartum women have been studied to determine whether depression and anxiety after the birth or adoption of a child can be associated with changes in the brain. Similarly, endocrinology studies have been limited to women who have recently given birth, and sometimes result in sexist stereotypes about both the causes and impacts of postpartum depression and anxiety, which may compound barriers to recovery. Studying only postpartum women’s brains and attempting to isolate a cause particular to women’s hormones contributes to damaging stereotypes of women, is likely to discourage men from seeking help, and to date does not seem to be productive in leading to effective treatment. Further, the lack of attention to social factors may result in less effective treatment. To improve diagnosis and treatment and to move towards a more equitable model of science, diagnosis of postpartum depression should examine the role of social factors, include others experiencing parental depression besides postpartum women as subjects, and avoid essentialist conclusions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".