The Impact of the COVID-19 Pandemic on Clinical Care: Considerations for Providing Virtual Evidence-Based Care to Youth With High Levels of Needle Fear
Bibliographic record
Abstract
Objective: Youth with high levels of needle fear are at risk for adverse health outcomes and poor compliance with general and specialty medical recommendations. With consideration of the COVID-19 pandemic and the increased availability of vaccines, recommendations for adapting and virtually delivering evidence-based interventions for youth with high levels of needle fear are of particular importance for pediatric psychologists. Thus, the purpose of this commentary is to provide an overview of evidence-based interventions and recommendations for pediatric psychologists seeking to adapt and virtually deliver evidence-based interventions to youth with high levels of needle fear. Conclusions: Although pediatric psychologists may face challenges when adapting and virtually delivering exposure-based interventions to youth and their families, the clinical benefits certainly outweigh the costs, particularly considering the increased availability of the COVID-19 vaccine for youth. Implications for Impact Statement Although it is unclear whether COVID-19 vaccinations will be approved for children under 12-years of age and whether subsequent mandates will be implemented, if left unaddressed needle fear may inadvertently perpetuate the pandemic, as it is anticipated that individuals with high levels of needle fear will be more likely to decline vaccination. Thus, with consideration of the COVID-19 pandemic and anticipated rollout of COVID-19 vaccinations for youth, recommendations for pediatric psychologists seeking to adapt and virtually deliver evidence-based interventions to youth with high levels of needle fear are of particular importance.
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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 | 0.058 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".