Problems with the recommendation to implement ACEs screening
Bibliographic record
Abstract
Thanks to the Journal for extending the opportunity for discussion about the topic of adverse childhood experiences (ACEs) screening and to Dr. Watson for her response to our letter (1,2). It is important to highlight that we are NOT contesting the need for health care providers to be aware of, and knowledgeable about, ACEs. Rather, our concern is specific to the recommendation to implement ACEs screening in clinical practice. The evidence used to inform any screening recommendation needs to be systematically evaluated using well-defined criteria. When current screening criteria, such as those outlined in a recent synthesis published in the Canadian Medical Association Journal (3), are applied to ACEs screening, the majority of criteria are simply not met. Recommendations to systematically screen for any particular exposure, symptom, sign, or illness, require clear operationalization and justification of each step in the proposed process. Identifying an important issue and a related tool is not sufficient. Furthermore, there is no evidence that systematic screening for individual items on ACEs questionnaires, or using an aggregate ACEs score, leads to better health outcomes. Additionally, the potential for more harm than benefit, from any well-intentioned screening, should not be ignored but considered across multiple domains (4).
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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 | 0.289 | 0.766 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.043 | 0.047 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".