Uncovering Adverse Childhood Experiences (ACEs) from Clinical Narratives within the Electronic Health Record
Bibliographic record
Abstract
Adverse Childhood Events (ACEs) are potentially traumatic events that occur in childhood (e.g., sexual abuse and maternal violence). Clinical research highlights the significant impact ACEs have on youth's mental health similar to other youth-related issues like traditional bullying and cyberbullying. However, research focused on the intersection of these two are limited. We report the results from a qualitative study that used electronic health record (EHR) data and clinical narratives from Parkview Behavioral Health hospital (n=719) to better understand the presentation of ACEs in patients who indicated cyber/bullying contributed to their inpatient hospital admission. Our deductive thematic analyses on the clinical narratives/notes and diagnoses highlight the connection of ACEs with cyber/bullying and other clinical diagnoses like depression, anxiety, PTSD, and ADD/ADHD. Additionally, our results point to potential impacts of the gender spectrum and other non-ACE indicators like adoption and the need for Department of Child Services (DCS). The outcome of this study provides distinct computational and clinical design guidelines for better collaborative decision making in healthcare, including the need for ACEs screening as standard-of-care within acute mental health settings. CAUTION: This paper includes graphic contents about adverse childhood traumas and events.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".