Abortion and mental health: guidelines for proper scientific conduct ignored
Bibliographic record
Abstract
Coleman presents her conclusions as 'an unbiased, quantitative analysis of the best available evidence' concerning the adverse mental health consequences of abortion.1 Huge numbers of papers by respectable researchers that have not found negative mental health consequences are ignored without comment.Not surprisingly, over 50% of the 'acceptable' studies she uses as her 'evidence' are those done by her and her colleagues Cougle and Reardon.The work of this group has been soundly critiqued not just by us 2,3 but by many others as being logically inconsistent and substantially inflated by faulty methodologies.As noted by the Royal Society of Obstetricians and Gynaecologists, 4 the authors consistently fail to differentiate between an association and a causal relationship and repeatedly fail to control for preexisting mental health problems.We note that Coleman did not include in her articles the publication by Munk-Olsen et al in the January 2011 New England Journal of Medicine, 5 which concluded that 'the rates of a first-time psychiatric contact before and after a first-trimester induced abortion are similar.This finding does not support the hypothesis that there is an overall increased risk of mental disorders after first-trimester induced abortion'.
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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 | 0.619 | 0.816 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.013 | 0.082 |
| Scholarly communication | 0.033 | 0.033 |
| Open science | 0.018 | 0.018 |
| Research integrity | 0.107 | 0.114 |
| Insufficient payload (model declined to judge) | 0.007 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".