Adverse Childhood Experiences: Survey of Resident Practice, Knowledge, and Attitude.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Adverse childhood experiences (ACEs) affect 20%-50% of adults and are associated with considerable adult chronic disease, unhealthy behavior, and early mortality. Physicians seldom identify this history although identification can improve health. Low screening rates are attributed to poor physician knowledge of ACEs and barriers to screening, including a lack of confidence to screen and insufficient training. Female physicians and physicians with personal ACE histories report more confidence to screen and fewer time barriers. Our aims were to identify resident screening practices, ACE knowledge, attitudes, and personal ACE histories and to determine preferred ways to learn more, if required. METHODS: Family medicine residents were surveyed, using a previously published survey. Items included ACE screening practices, ACE knowledge, attitudes, and personal ACE histories. RESULTS: The response rate was 97% (112/115), and 58% were female. Two percent of residents screened females and males at the first visit, thereafter residents screened women (6.3%) more than men (0.9%). One third of residents identified the correct prevalence of ACE in women and one tenth male prevalence. Unhealthy behaviors or physical chronic disease were not associated with ACE histories. Sixty-five percent of residents were not confident to screen. Twenty-nine percent of residents reported a trauma history. Eighty percent believed it was their role to screen. Formal medical training to screen was received by 45.5%; only five residents recalled training during residency. CONCLUSIONS: Resident ACE screening rates were extremely low. Physician educational initiatives are recommended to increase confidence to screen and actual screening prior to graduation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".