Full and Partial Facial Affect Recognition in Pediatric Brain Tumour Survivors and Typically Developing Children Following COVID-19 Pandemic
Bibliographic record
Abstract
Affect recognition has emerged as a potential mechanism underlying the social competence challenges experienced by pediatric brain tumour survivors (PBTSs). However, many social interactions were altered during the pandemic, with the widespread use of masking potentially impacting affect recognition abilities. Here, we examine affect recognition in PBTSs and typically developing youth (TD) after the onset of the global pandemic. Twenty-three PBTSs and 24 TD between 8 and 16 years old were recruited and completed two performance-based affect recognition tasks (full and partial facial features) and a self-reported questionnaire on mask exposure in their social interactions. Their parents completed parent proxy questionnaires on their child’s social adjustment and sociodemographics. The scores between the PBTSs and TD did not differ significantly in full (t(45) = 1.33, p = 0.19, d = 0.39, 95% CI [−0.69, 3.40]) or partial (t(37.36) = 1.56, p = 0.13, d = 0.46, 95% CI [−0.47, 3.60]) affect recognition, suggesting similar affect recognition between the two groups. These skills were also not significantly correlated with social adjustment or mask exposure (p > 0.05). However, the combined sample had significantly better scores in affect recognition when exposed to partial facial cues versus full. Additionally, participants obtained lower scores on a measure of full facial affect recognition and higher scores on a measure of partial affect recognition compared to pre-pandemic data. The pandemic may have influenced affect recognition across youth, underscoring the importance of further research into its lasting impact on the social competence of youth.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".