eSCCIP-SP: Adapting an eHealth Intervention for Spanish-Speaking Parents of Children With Cancer
Bibliographic record
Abstract
Objective: Psychosocial interventions for Latinx parents of children with cancer (PCC) are scarce, despite documented psychosocial risk in this population. This article describes the development of El Programa Electrónico de Intervención para Superar el Cancer Competentemente (eSCCIP-SP), a Spanish-language psychosocial intervention for PCC, adapted from an existent eHealth intervention for English-speaking PCC (eSCCIP). Methods: The adaptation process was multifaceted and followed best practice guidelines from the National Standards for Culturally and Linguistically Appropriate Services literature. eSCCIP intervention materials were first translated and reviewed by Spanish-speaking members of the study team, consultants, and medical interpreters. New video materials were created with Spanish-speaking families, and cultural adaptations were made to intervention materials. The completed intervention was refined via Think Aloud Testing with Spanish-speaking PCC and by expert review. Results: A user-centered, multistep adaptation process was used to develop and evaluate eSCCIP-SP. Results from Think Aloud Testing were positive, with the majority of suggested comments related to phrasing and edits. Participants provided positive feedback about the intervention and its potential impact. Conclusions: The rigorous development of eSCCIP-SP provides a model for adapting psychosocial interventions for Spanish-speaking PCC. Initial results suggest that eSCCIP-SP is an acceptable psychosocial intervention. Implications for Impact Statement eSCCIP-SP is a psychosocial eHealth intervention for Spanish-speaking parents and caregivers of children with cancer. This intervention has the potential to reduce a critical gap in care in terms of supporting this important population by providing flexible, accessible, high-quality psychosocial care to Spanish-speaking PCC.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".