Development of a Screening Instrument to Improve Access to Follow-Up Care for Children With Complex Medical Conditions: The Virtually Guided Infant Developmental Abilities Tool
Bibliographic record
Abstract
IMPORTANCE: Telerehabilitation helps overcome barriers to neonatal follow-up care for children with complex medical conditions; however, existing developmental assessment tools are not suitable for this virtual medium. OBJECTIVE: To create the Virtually Guided Infant Developmental Abilities (VIDA) screening tool, intended for children ages 1-18 mo. DESIGN: The VIDA screening tool was developed using the Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research framework. Phase 1 involved defining developmental domains, generating items via a literature scoping review, and assessing content validity through a modified Delphi survey. Phase 2 included a pilot test to ensure the tool's clarity and feasibility. SETTING: The VIDA screening tool was developed at the Kingston Health Sciences Centre in Kingston, Ontario, Canada, in a tertiary care setting. PARTICIPANTS: In Phase 1, which focused on content validity, a modified Delphi survey technique was undertaken with a total of 12 experts (10 occupational therapists, one physiotherapist, and one neonatologist). In Phase 2, which focused on scale development, a pilot test study was conducted with two occupational therapists and three families whose children necessitated neonatal follow-up care. RESULTS: The final VIDA screening tool includes 81 items that assess a child's gross motor, fine motor, self-care, and social communication and cognitive skills through prompted responses or parent-reported observations. CONCLUSIONS AND RELEVANCE: The VIDA screening tool was developed to increase accessibility to neonatal follow-up care for children with complex medical conditions. The tool consists of observations of a child's developmental abilities, made through a virtual platform, that can be used by most health professionals to identify children who need further developmental evaluations. Plain-Language Summary: The authors developed the Virtually Guided Infant Developmental Abilities (VIDA) screening tool to address the lack of virtual developmental assessments. It was created in accordance with the Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research framework. Phase 1 focused on item development and content validity and involved a modified Delphi survey administered to 12 health professional experts, including occupational therapists, a physiotherapist, and a neonatologist. Phase 2 involved scale development through a pilot test with two occupational therapists and three families whose children required neonatal follow-up care. The finalized VIDA tool includes two parts: Part 1 entails an intake form to gather information on parents' concerns about their children's development, and Part 2 includes 81 items that assess gross motor, fine motor, self-care, and social communication and cognitive skills through parent-reported observations or prompted responses. Designed for use by most health professionals, the VIDA tool improves accessibility to follow-up care by enabling virtual evaluations of developmental abilities, thereby helping identify children who need in-depth developmental assessments. The VIDA screening tool offers occupational therapists a promising approach to support early developmental monitoring of high-risk infants.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".