Integrating Telehealth Into Neurodevelopmental Assessment: A Model From the Cardiac Neurodevelopmental Outcome Collaborative
Bibliographic record
Abstract
OBJECTIVE: In the wake of the COVID-19 pandemic, psychologists were pushed to look beyond traditional in-person models of neurodevelopmental assessment to maintain continuity of care. A wealth of data demonstrates that telehealth is efficacious for pediatric behavioral intervention; however, best practices for incorporating telehealth into neurodevelopmental assessment are yet to be developed. In this topical review, we propose a conceptual model to demonstrate how telehealth can be incorporated into various components of neurodevelopmental assessment. METHODS: Harnessing existing literature and expertise from a multidisciplinary task force comprised of clinicians, researchers, and patient/parent representatives from the subspecialty of cardiac neurodevelopmental care, a conceptual framework for telehealth neurodevelopmental assessment was developed. Considerations for health equity and access to care are discussed, as well as general guidelines for clinical implementation and gaps in existing literature. RESULTS: There are opportunities to integrate telehealth within each stage of neurodevelopmental assessment, from intake to testing, through to follow-up care. Further research is needed to determine whether telehealth mitigates or exacerbates disparities in access to care for vulnerable populations as well as to provide evidence of validity for a wider range of neurodevelopmental measures to be administered via telehealth. CONCLUSIONS: While many practices are returning to traditional, face-to-face neurodevelopmental assessment services, psychologists have a unique opportunity to harness the momentum for telehealth care initiated during the pandemic to optimize the use of clinical resources, broaden service delivery, and increase access to care for pediatric neurodevelopmental assessment.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".