Pregnant Women’s Perceptions of Harms and Benefits of Mental Health Screening
Bibliographic record
Abstract
BACKGROUND: A widely held concern of screening is that its psychological harms may outweigh the benefits of early detection and treatment. This study describes pregnant women's perceptions of possible harms and benefits of mental health screening and factors associated with identifying screening as harmful or beneficial. METHODS: This study analyzed a subgroup of women who had undergone formal or informal mental health screening from our larger multi-site, cross-sectional study. Pregnant women >16 years of age who spoke/read English were recruited (May-December 2013) from prenatal classes and maternity clinics in Alberta, Canada. Descriptive statistics were generated to summarize harms and benefits of screening and multivariable logistic regression identified factors associated with reporting at least one harm or affirming screening as a positive experience (January-December 2014). RESULTS: Overall study participation rate was 92% (N = 460/500). Among women screened for mental health concerns (n = 238), 63% viewed screening as positive, 69% were glad to be asked, and 87% took it as evidence their provider cared about them. Only one woman identified screening as a negative experience. Of the 6 harms, none was endorsed by >7% of women, with embarrassment being most cited. Women who were very comfortable (vs somewhat/not comfortable) with screening were more likely to report it as a positive experience. LIMITATIONS: Women were largely Caucasian, well-educated, partnered women; thus, findings may not be generalizable to women with socioeconomic risk. CONCLUSIONS: Most women perceived prenatal mental health screening as having high benefit and low harm. These findings dispel popular concerns that mental health screening is psychologically harmful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".