Usability Testing of an Internet-Based Responsive Parenting Program for Caregivers of Young Survivors of Childhood Cancer Living in Rural and Appalachian Communities: Mixed Methods Study
Bibliographic record
Abstract
Background: Young survivors of childhood cancer diagnosed and treated before the age of 7 years are at heightened risk of developmental difficulties. As a result, their caregivers may experience stress as they navigate various educational and health care systems while advocating for their child's needs. To our knowledge, there are no tailored early interventions or support programs to address the unique and multifaceted needs of young survivors of childhood cancer and their caregivers. Digital platforms can provide anticipatory guidance and developmental resources, which may be especially impactful for families at increased risk for health and educational disparities, particularly in underserved rural and Appalachian areas. Objective: This mixed methods study describes the usability testing of an internet-based responsive parenting program for caregivers of young survivors of childhood cancer, called the Preparing for Life and Academics for Young Survivors (PLAY) program. Methods: Twelve caregivers of young survivors of childhood cancer (9 biological parents, 2 grandfathers, and 1 legal guardian; 33% rural, 33% Appalachian, and 83% White) participated in an online usability session with a task-based, think-aloud method. This session was followed by completion of the System Usability Scale (SUS), responses to reaction cards to describe their experiences with the platform, and a semistructured interview. Two trained coders used rapid analysis and consensus methods to identify themes. Results: Overall, caregivers found the platform easy to use (92% SUS >68; mean 82.2, SD 14.45). Most caregivers provided positive feedback in response to reaction cards (eg, useful, appealing, and valuable) to describe the platform. Qualitative findings highlighted that most caregivers perceived the platform as "easy to use," and several participants described it as a helpful educational tool. Participants noted that potential platform improvements should include accessibility features (eg, closed captioning for videos), more tailored content, and usability on mobile devices. Conclusions: With further improvements, digital platforms such as PLAY may be a promising avenue to bridge access to care for high-need groups and ultimately improve child neurodevelopmental outcomes.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".