Routine primary care screening for intimate partner violence and other adverse psychosocial exposures: what’s the evidence?
Bibliographic record
Abstract
BACKGROUND: Family physicians and other primary care practitioners are encouraged or expected to screen for an expanding array of concerns and problems including intimate partner violence (IPV). While there is no debate about the deleterious impact of violence and other adverse psychosocial exposures on health status, the key question raised here is about the value of routine screening in primary care for such exposures. DISCUSSION: Several characteristics of IPV have led to consideration for routine IPV screening in primary care and during other healthcare encounters (e.g., emergency room visits) including: its high prevalence, concern that it may not be raised spontaneously if not prompted, and the burden of suffering associated with this exposure. Despite these factors, there are now three randomized controlled trials showing that screening does not reduce IPV or improve health outcomes. Yet, recommendations to routinely screen for IPV persist. Similarly, adverse childhood experiences (ACEs) have several characteristics (e.g., high frequency, predictive power of such experiences for subsequent health problems, and concerns that they might not be identified without screening) suggesting they too should be considered for routine primary care screening. However, demonstration of strong associations with health outcomes, and even causality, do not necessarily translate into the benefits of routine screening for such experiences. To date, there have been no controlled trials examining the impact and outcomes - either beneficial or harmful - of routine ACEs screening. Even so, there is an expansion of calls for routine screening for ACEs. While we must prioritize how best to support and intervene with patients who have experienced IPV and other adverse psychosocial exposures, we should not be lulled into a false sense of security that our routine use of "screeners" results in better health outcomes or less violence without evidence for such. Decisions about implementation of routine screening for psychosocial concerns need similar rigorous debate and scrutiny of empirical evidence as that recommended for proposed physical health screening (e.g., for prostate and breast cancer).
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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 | 0.028 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".