Identifying when children with visual impairment share attention: A novel protocol and the impact of visual acuity
Bibliographic record
Abstract
BACKGROUND: In Coordinated Joint Engagement (CJE), children acknowledge that they and their social partners are paying attention to the same object. The achievement of CJE, critical for healthy development, is at risk in infants with visual impairment (VI). Research on CJE in these children is limited because investigators use a child's gaze switch between social partner and object to index CJE. Research is needed that identifies CJE in children with VI using behaviors that do not require normal vision and that explores the relationship between CJE and visual function. This study aimed to (a) develop a protocol for identifying CJE in children with VI, and (b) explore the relationship between CJE and infants' visual acuity (VA) and contrast sensitivity (CS), measured with Preferential Looking (PL) techniques and Visual Evoked Potential (VEP). METHODS: A protocol that included 9 indices of CJE that did not require normal vision was developed to code videos of 20 infants with VI (mean age =1 year, 6 months, 27 days) and their caregivers. The percentage of CJE episodes in which each index was observed was calculated. Inter-coder reliability was measured using Cohen's Kappa. Linear regression analysis was used to examine the relationship between the infants' visual function and CJE. RESULTS: Inter-rater reliability between a first coder and each of two second coders were 0.98 and 0.90 for determining whether the child participated in CJE. The following indices were observed the most (in 43-62 % of CJE): child's body orientation to caregiver, gaze switch between caregiver and object, and vocalization to caregiver. The only significant model included VA (measured with PL) as a single predictor and explained 26.8 % of the variance in CJE. CONCLUSIONS: The novel protocol can be used to identify CJE in children with VI with good inter-coder reliability. The data suggest that children with lower VA exhibited less CJE.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".