An Assessment of Harm in Adults—Adverse Childhood Experiences Screening in Primary Care: A Survey-Based Study
Bibliographic record
Abstract
The Adverse Childhood Experiences Questionnaire (ACE-Q) screens for adverse childhood experiences (ACEs), which are linked to increased disease risk. Although pediatric studies report no adverse effects of ACE-Q use, primary care data is limited. This study examined adult patients’ experiences with ACE-Q screening in primary care. Adults (18+) at a primary care center in London, Ontario, completed the ACE-Q and a follow-up questionnaire evaluating ACE screening experience. Correlations assessed relationships between ACE-Q scores and follow-up responses. Among 260 participants, 81% reported at least one ACE. Most (82%) felt comfortable discussing stressful childhood experiences with their healthcare provider. Higher ACE scores were associated with increased discomfort (r s = −0.166, P = 0.007), feeling upset by the ACE-Q (r s = 0.173, P = 0.005), and greater interest in learning about ACEs (r s = 0.177, P = 0.004). Overall, ACE-Q screening in primary care was generally well-received, with most patients recognizing its relevance despite some discomfort. These findings highlight the potential for integrating ACE screening into routine primary care to address long-term health risks. Further research is needed to confirm findings and optimize screening practices.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".