Family-professional collaborative intervention via telehealth with an infant with Down syndrome and visual impairment: a case report
Bibliographic record
Abstract
BACKGROUND: Collaborative interventions, involving partnerships between professionals and families, make it possible to actively engage families in the rehabilitation process. However, no study was found that used a physiotherapy intervention via telehealth for infants with Down syndrome (DS). OBJECTIVE: Describe the case report of a family-professional collaborative intervention via telehealth for an infant with Down syndrome and visual impairment during the COVID-19 pandemic. CASE DESCRIPTION: A male infant five months old participated in a collaborative intervention for a period of eight weeks. Three goals were set with the family, and a goal-oriented home program, involving visual stimulation strategies, was organized. OUTCOMES: Two goals were achieved. The family scored changes in performance and satisfaction with the infant's performance according to the adapted Canadian Occupational Performance Measure. Improvement of motor skills was verified by Alberta Infant Motor Scale and changes in mobility and daily activities performance were measured by the Pediatric Evaluation of Disability Inventory - Computer Adaptive Test. CONCLUSION: The use of family-professional collaborative intervention via telehealth during the COVID-19 pandemic shows promising results for achieving the goals established by the family collaboratively with the physical therapists.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".