Telepsychotherapy with children and families: Lessons gleaned from two decades of translational research.
Bibliographic record
Abstract
The novel coronavirus, COVID-19, has led to sweeping changes in psychological practice and the concomitant rapid uptake of telepsychotherapy. Although telepsychotherapy is new to many clinical psychologists, there is considerable research on telepsychotherapy treatments. Nearly 2 decades of clinical research on telepsychotherapy treatments with children with neurological conditions has the potential to inform emerging clinical practice in the age of COVID-19. Toward that end, we synthesized findings from 14 clinical trials of telepsychotherapy problem-solving and parent-training interventions involving more than 800 children and families with diverse diagnoses, including traumatic brain injury, epilepsy, brain tumors, congenital heart disease, and perinatal stroke. We summarize efficacy across studies and clinical populations and report feasibility and acceptability data from the perspectives of parents, children, and psychotherapists. We describe adaptation for international contexts and strategies for troubleshooting technological challenges and working with families of varying socioeconomic strata. The extensive research literature reviewed and synthesized provides considerable support for the utility of telepsychotherapy with children with neurological conditions and their families and underscores its high level of acceptability with both diverse clinical populations and providers. During this period of heightened vulnerability and stress and reduced access to usual supports and services, telepsychotherapy approaches such as online family problem-solving treatment and online parenting skills training may allow psychologists to deliver traditional evidence-based treatments virtually while preserving fidelity and efficacy.
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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 | 0.033 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".