An online ASD learning module for pediatric health care professionals
Bibliographic record
Abstract
Purpose Youth with autism spectrum disorder (ASD) often have co-occurring health care needs and are likely to come into contact with several health care professionals over their lives. At the hospital, youth with ASD may require specialized supports to optimize health care experiences and for medical services to be delivered safely. At present, there is a limited understanding of how to best support this patient population. The purpose of this paper is to develop, implement, and evaluate an online training module for hospital staff about ASD. Design/methodology/approach To evaluate participants’ perceived utility of the learning tool, a post-module survey was administered. Findings In all, 102 health care professionals and other hospital staff completed the training and evaluation measure. Majority of participants had prior ASD-focused education (66 percent) and had experience working with at least 20 youths with ASD (57 percent). Majority of participants (88 percent) perceived the information from the module to be helpful in their daily work and reported that they learned something new (63 percent). Participants were interested in receiving additional ASD online module training opportunities on topics including: hands-on behavior management strategies, in-hospital resources, guidance on treatment adherence, and ASD training geared specifically to protection services staff. Originality/value The results from this evaluation have important practice implications for hospital staff working with patients with ASD and their families. Evidence-based strategies were easily accessible for staff and the module can be feasibly built upon and expanded as well as disseminated beyond the current hospital setting.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".