A Viewpoint: Validation of Self-Reported Anxiety Screening Scales in Perinatal Populations– Outstanding Issues in the Validation Research Agenda
Bibliographic record
Abstract
In the absence of perinatal anxiety screening, clinically significant symptoms of anxiety remain under-detected and undertreated during perinatal period. Perinatal anxiety has a significant and far reaching impact on mothers and their children. There are well established recommendations for routine mental health screening during the perinatal period, yet less than one in five of providers routinely screen and one in seven women receive the mental health intervention they need. The validity of anxiety screening scales needs to be ascertained by criterion validity parameters such as sensitivity, specificity, and predictive values. Criterion validity, which is assessed against a reference standard, allows clear interpretation of screening results, supporting their integration with clinical care pathways. However, few existing validation studies of anxiety scales in perinatal populations examined criterion validity. Construct validity of anxiety scales, the focus of existing validation studies, does not use reference standard; thus, it lacks clear clinical interpretability for screening application. Future validation studies should focus on assessing the criterion validity of anxiety scales in perinatal populations. This will help nurses, midwives, and other members of interdisciplinary teams to clarify scale validity, interpretation, and utility for screening women for anxiety in the perinatal period.
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 | 0.130 | 0.303 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.024 | 0.039 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".