eHealth Preferences for Parents of Young Children Diagnosed with Neurodevelopmental Disorders (NDDs) and/or Mental Health (MH) Disorders
Bibliographic record
Abstract
Background: This study aimed to describe preferences for e-health services among parents/caregivers of young children diagnosed with neurodevelopmental disorders (NDDs) and/or mental illnesses (MI).Methods: Parents/caregivers of children ages 0-5 completed an anonymous survey via the crowdsourcing platform, AskingCanadians. The survey asked participants about their preferences in e-health services for supporting their children. Results: Out of 606 surveyed parents/caregivers, 26.4% had a child diagnosed with a NDD or MI, self-reported by the parents/caregivers. There were many differences in preferences for e-health services between parents of children with one NDD/MI diagnosis, multiple NDDs/MIs, and no diagnoses. Parents of children with multiple NDDs/MIs preferred a mix of peer- and expert-driven programs and more structured and asynchronous program formats compared to the other groups, who preferred simply expert-driven programs.Conclusions: Tailoring e-health programs based on parents of children with NDDs’/MIs’ preferences could enhance e-health program effectiveness, reduce dropout rates, and improve program development.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".