90 School-based Implementation of Educational and Neurocognitive Interventions in Children with Neurodevelopmental Disorders.
Bibliographic record
Abstract
Objective: Children with neurodevelopmental disorders (NDDs) commonly experience attentional and executive function (EF) difficulties that are negatively associated with academic success, psychosocial functioning, and quality of life. Access to early and consistent interventions is a critical protective factor and there are recommendations to deliver cognitive interventions in schools; however, current cognitive interventions are expensive and/or inaccessible, particularly for those with limited resources and/or in remote communities. The current study evaluated the school-based implementation of two game-based interventions in children with NDDs: 1) a novel neurocognitive attention/EF intervention (Dino Island; DI), and 2) a commercial educational intervention (Adventure Academy; AA). DI is a game-based attention/EF intervention specifically developed for children for delivery in community-based settings. Participants and Methods: Thirty five children with NDDs (ages 5-13 years) and 17 EAs participated. EAs completed on-line training to deliver the interventions to assigned students at their respective schools (3x/week, 40-60 minutes/session, 8 weeks, 14 hours in total). We gathered baseline child and EA demographic data, completed pre-intervention EA interviews, and conducted regular fidelity checks throughout the interventions. Implementation data included paper-pencil tracking forms, computerized game analytic data, and online communications. Results: Using a mixed methods approach we evaluated the following implementation outcomes: fidelity, feasibility, acceptability, and barriers. Overall, no meaningful between-group differences were found in EA or child demographics, except for total number of years worked as an EA (M = 17.18 for AA and 9.15 for DI; t (22) = - 4.34, p < .01) and EA gender (χ2 (1) = 6.11, p < .05). For both groups, EA age was significantly associated with the number of sessions played [DI (r = .847, p < .01), AA (r = .986, p < .05)]. EAs who knew their student better completed longer sessions [DI (r = .646), AA (r = .973)], all ps < .05]. The number of years worked as an EA was negatively associated with the total intervention hours for both groups. Qualitative interview data indicated that most EAs found DI valuable and feasible to deliver in their classrooms, whereas more implementation challenges were identified with AA. Barriers common to both groups included technical difficulties (e.g., game access, internet firewalls), environmental barriers (e.g., distractions in surroundings, time of the year), child factors (e.g., lack of motivation, attentional difficulties, frustration), and game-specific factors (e.g., difficulty level progression). Barriers specific to DI included greater challenges in motivating children as a function of difficulty level progression. Furthermore, given the comprehensive nature of training required for delivery, EAs needed a longer time to complete the training for DI. Nevertheless, many EAs in the DI group found the training helpful, with a potential to generalize to other children in the classroom. Conclusions: The availability of affordable, accessible, and effective cognitive intervention is important for children with NDDs. We found that delivery of a novel cognitive intervention by EAs was feasible and acceptable, with similarities and differences in implementation facilitators/barriers between the cognitive and commercialized academic intervention. Recommendations regarding strategies for successful school-based implementation of neurocognitive intervention will be elaborated on in the poster.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".