Physicians’ knowledge and practices regarding screening adult patients for adverse childhood experiences: a survey
Bibliographic record
Abstract
Abstract Background Adverse Childhood Experiences (ACEs) are common and associated with many illnesses. Most physicians do not routinely screen for ACEs. We aimed to determine if screening is related to knowledge or medical specialty, and to assess perceived barriers. Methods Physicians in Ontario, Canada completed an online survey in 2018–2019. Data were analyzed in 2019. Results Participants were 89 family physicians, 46 psychiatrists and 48 other specialists. Participants screened for ACEs “never or not usually” ( N = 58, 31.7%), “when indicated” ( N = 67, 36.6%), “routinely” ( N = 50, 27.3%) or “other” ( N = 5, 2.7%). Screening was strongly associated with specialty (Chi 2 = 181.0, p < .001). The modal responses were: family physicians - “when indicated” (66.3%), psychiatrists - “routinely” (91.3%), and other specialists - “never or not usually” (77.1%). Screening was not related to knowledge of prevalence of ACEs, or of the link between ACEs and mental health, but was significantly associated with knowing that ACEs are associated with physical health. Knowing that ACEs are linked to stroke, ischemic heart disease, COPD, and diabetes predicted greater screening (Chi 2 15.0–17.7, each p ≤ .001). The most prevalent perceived barriers to screening were lack of mental health resources (59.0%), lack of time (59.0%), concern about causing distress (49.7%) and lack of confidence (43.7%). Conclusions Enhancing knowledge about ACEs’ negative influence on physical illness may increase screening. Efforts to promote screening should address concerns that screening is time-consuming and will increase referrals to mental health resources. Education should focus on increasing confidence with screening and with managing patient distress.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".