Caregiver Experiences, Healthcare Provider Perspectives and Child Outcomes with Virtual Care in a Neonatal Neurodevelopmental Follow-Up Clinic: A Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Caregiver and healthcare provider perspectives of virtual care have not been explored in depth in the literature for neonatal follow-up clinics. Our objective was to evaluate caregivers' and healthcare providers' perspectives and compare neurodevelopmental outcomes of preterm neonates before and after implementing virtual care during the SARS-CoV-2 pandemic. METHODS: Semi-structured interviews were conducted with families and healthcare providers, designed and analyzed using phenomenological qualitative methods. A retrospective cohort study was conducted to evaluate and compare neurodevelopmental characteristics of two preterm cohorts, one before ("in-person") and after ("virtual") virtual care. RESULTS: Three themes were identified: increased confidence in in-person assessments, adequate delivery of information using virtual platforms and a preference for specialized care through the neonatal follow-up clinic. A total of 252 infants born preterm, 104 infants in the in-person group and 148 infants in the virtual group, were included in the study. The adjusted odds ratio (aOR) of cerebral palsy was lower when virtual care was used compared to in-person assessments (aOR = 0.11, 95% CI 0.01-0.98) while the adjusted odds of cognitive delay measured by in-person standardized testing were higher (aOR = 2.78, 95% CI 1.25-6.19). CONCLUSIONS: Caregivers and healthcare providers prefer in-person assessments for comprehensive developmental support. It may be more challenging to detect subtle cognitive differences using caregiver-reported measures. Cerebral palsy may be missed when assessments are completed virtually.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".