Virtual Thoughts: Provider Voices on Teleintervention with Families of Young Children with Visual Impairment
Bibliographic record
Abstract
Abstract Objective The COVID-19 pandemic event created an opportunity for all early intervention (EI) practitioners to look at the development of remote practice standards and learn about the diverse ways to serve and support families. In addressing the use of teleintervention, it is critical for the field of visual impairment to learn which implementation strategies were most effective and what is practical moving forward from those who provide the EI services. Methods This study incorporated an event-driven mixed-methods methodology that included a quantitative online survey to collect broader national viewpoints and dynamic data from focus groups that took an evolutionary approach to the changing perspectives of the participants following COVID-19 on their teaching practices using teleintervention. Results The results of this study have identified positive and problematic features of teleintervention services when delivered to families of very young children with visual impairment. Discussion Exploring the successes of organizations and individuals providing these EI services leads to a greater understanding of the development of a high-quality protocol for teleintervention for this population and equity for families. Application for Practitioners The deep dive into the “virtual thoughts” of providers across the country allows them to voice their triumphs and concerns on developing suggestions for quality service delivery and preservice needs.
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".