Introduction to the special issue on contemporary and cross-cutting evidence-based interventions in pediatric psychology
Bibliographic record
Abstract
In a time where misinformation abounds and access to care is precarious, research on evidence-based interventions is more important than ever. Pediatric psychologists rely on the availability of evidence to guide clinical decision-making in supporting the mental health and well-being of children with acute and chronic illnesses. Interventions in pediatric psychology have stronger evidence for efficacy than many pharmacological and other health interventions. This evidence base has facilitated advocacy for the inclusion of psychologists in medical teams and for funding to improve access to psychological care for children with acute and chronic medical needs, and likely accounts for many of the documented positive outcomes of integrating pediatric psychologists in children’s health care (Janicke & Hommel, 2016; McGrady, 2018; Pereira et al., 2021). Systematic review and meta-analysis are a cornerstone of this work. As pediatric psychology trials are highly cost- and resource-intensive, and many pediatric populations are small, achieving sufficient power to detect effects is often a challenge for individual trials. Similarly, single trials are often constrained by geography, demographics of the local population, and availability of diverse therapists, limiting generalizability beyond the study site. Reviews are critical to summarize and pool the existing evidence base to determine the level of confidence we can have in the efficacy of an intervention in our own clinical setting, as well as offering an opportunity for critical analysis of research quality.
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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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".