Bibliographic record
Abstract
Play is an everyday activity during childhood and is thought to be essential for development (Brodin, 1999). Nevertheless, many children with autism tend to engage in rigid play behavior, often only playing with the same toys in the same way or not playing flexibly enough to include other children or parents in their playtime (Kasari et al., 2011). This rigidity may, in turn, impede their ability to form social relationships with their peers and hinder the development of communication skills. Accordingly, increasing creative play behavior in children with autism may decrease rigid and solitary play and increase communication skills and social relationships. One method that may be useful for improving this skill is Teaching with Acoustical Guidance (TAGteach). This intervention involves shaping behavior through positive reinforcement using an auditory stimulus (Persicke et al., 2013). Given the behavioral principles that underlie its methodology, we predicted that TAGteach would be useful for increasing creative play behavior in two children with autism. A non-concurrent multiple baseline across participants design was used to evaluate the outcomes. Because of the COVID-19 pandemic, the study was run via encrypted real-time video conferencing. Social validity of the intervention was assessed at the end of the study. Overall, we found a slight increase in creative play behavior across sessions for both participants. Additionally, the individuals who implemented the TAGteach intervention rated the intervention moderately to extremely positive on a post-test social validity measure. These findings offer initial insight into the use of TAGteach via telehealth for children with autism. Department: Psychology Faculty Mentors: Dr. Russ Powell and Miranda Macauley
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".