Online Delivery of Interprofessional Adverse Childhood Experiences Training to Rural Providers: Usability Study
Bibliographic record
Abstract
Background: The population health burden of adverse childhood experiences (ACEs) reflects a critical need for evidence-based provider training. Rural children are also more likely than urban children to have any ACEs. A large proportion of providers are unaware of the detrimental effects of ACEs. There is a significant documented need for training providers about ACEs and trauma-informed care, in addition to a demand for that training. Objective: The objective was to develop, implement, and evaluate an online ACEs training curriculum tailored to Missouri providers, particularly those in rural areas given the higher prevalence of ACEs. Methods: From July 2021 to June 2022, we conducted literature reviews and environmental scans of training videos, partner organizations, clinical practice guidelines, and community-based resources to curate appropriate and tailored content for the course. We developed the ACEs training course in the Canvas learning platform (Instructure) with the assistance of an instructional designer and media designer. The course was certified for continuing medical education, as well as continuing education for licensed professional counselors, psychologists, and social workers. Recruitment occurred via key stakeholder email invitations and snowball recruitment. Results: Overall, 135 providers across Missouri requested enrollment, with 72.6% (n=98) enrolling and accessing the training. Of the latter, 49% (n=48) completed course requirements, with 100% of respondents agreeing that the content was relevant to their work, life, or practice; they intend to apply the content to their work, life, or practice; they feel confident to do so; and they would recommend the course to others. Qualitative responses supported active intent to translate knowledge into practice. Conclusions: This study demonstrated the feasibility, acceptability, and effectiveness of interprofessional workforce ACEs training. Robust interest statewide reflects recognition of the topic's importance and intention to translate knowledge into practice.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".