A Comparison of the Effectiveness, Efficiency, and Post-Training Outcomes of Traditional Behavioral Skills Training and Asynchronous Remote Training
Bibliographic record
Abstract
While applied behavior analysis (ABA) is the most commonly recommended therapy for individuals with Autism spectrum disorders (New York State Department of Health, 1999; Surgeon General, 1999), there is a significant lack of board certified behavior analysts (BCBAs; Bethune & Kiser, 2017; Maglione, Kadiyala, Kress, Hastings, & OʹHanlon, 2016). Telehealth may help to increase the availability of training in behavior analytic procedures, however, BCBAs have been slow to adopt remote training measures (Tomlinson, Gore, & McGill, 2018). This may be due to the in-vivo training requirements of common behavior analytic training procedures. This research compares traditional, face-to-face behavioral skills training (BST) to remote training in order to determine if the success attributed to BST is replicable through remote education. This research also investigates how the post-training outcomes of traditional BST compare to asynchronous online training with video modeling and feedback. In order to evaluate this, the researcher trained two groups on multiple stimulus without replacement preference assessment procedures. The first group received traditional behavioral skills training in person with immediate feedback while the second group received all training through pre-recorded video with self-monitoring and delayed performance feedback. Results indicated that both methods were similarly effective with in-person training being slightly more efficient for trainees while remote training was significantly more efficient for the trainer. The research not only evaluated the post-training outcomes of both methodologies but also examined the social validity of both training models. Both procedures had high social validity indicating that these methods could be used in the future. These results not only add to the body of literature on remote training in behavior analytic interventions, but also look at areas of improvement in order to make future training of behavior analysts more effective. More broadly, this research could help to disseminate ABA and remote education so people in more remote locations have equitable access to services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".