Family Perspectives on In-Home Multimodal Longitudinal Data Collection for Children Who Function Across the Developmental Spectrum
Bibliographic record
Abstract
OBJECTIVE: Quality child health research requires multimodal, multi-informant, longitudinal tools for data collection to ensure a holistic description of real-world health, function, and well-being. Although advances have been made, the design of these tools has not typically included community input from families with children whose function spans the developmental spectrum. METHODS: We conducted 24 interviews to understand how children, youth, and their families think about in-home longitudinal data collection. We used examples of smartphone-based Ecological Momentary Assessment of everyday experiences, activity monitoring with an accelerometer, and salivary stress biomarker sampling to help elicit responses. The children and youth who were included had a range of conditions and experiences, including complex pain, autism spectrum disorder, cerebral palsy, and severe neurologic impairments. Data were analyzed using reflexive thematic analysis and descriptive statistics of quantifiable results. RESULTS: Families described (1) the importance of flexibility and customization within the data collection process, (2) the opportunity for a reciprocal relationship with the research team; families inform the research priorities and the development of the protocol and also benefit from data being fed back to them, and (3) the possibility that this research approach would increase equity by offering accessible participation opportunities for families who might otherwise not be represented. Most families expressed interest in participating in in-home research opportunities, would find most methods discussed acceptable, and cited 2 weeks of data collection as feasible. CONCLUSION: Families described diverse areas of complexity that necessitate thoughtful adaptations to traditional research designs. There was considerable interest from families in active engagement in this process, particularly if they could benefit from data sharing. This feedback is being incorporated into pilot demonstration projects to iteratively codesign an accessible research platform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".