Questioning the use of adverse childhood experiences (ACEs) questionnaires
Bibliographic record
Abstract
Adverse childhood experiences (ACEs) are increasingly recognized as important predictors of poor health outcomes. In response, there is increasing application of ACEs questionnaires in clinical practice and population health surveys. Such efforts are often justified as approaches to identify ACEs, components of trauma-informed care, and/or measures to determine prevalence within epidemiological research. Unfortunately, such measures are often used without evaluating the strengths and limitations of the measures themselves. One of the most commonly used ACEs questionnaires is a ten-question version (ACEs-10), that is composed of two clusters - one asking about different types of child maltreatment, and the other asking select questions about household challenges. Unfortunately, both this questionnaire and its derivatives have substantial drawbacks that warrant careful consideration about their use. Problems include limited item coverage, collapsing of items and response options, a simplistic scoring approach, and the lack of psychometric assessment. These deficiencies are inconsistent with the standards expected for use of measures in healthcare services and research. Given these deficiencies, we recommend that these limitations are addressed before further use of ACEs-10, and its derivatives, for either clinical or research purposes.
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 | 0.036 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".