Bibliographic record
Abstract
Background and Aims: A cancer disease during the developmental age can lead to failure to regulate and express emotions with the onset of psychopathology in children. This study aims to: 1. screen children's emotional and social competence and behavioral symptoms during the acute phase of treatment and 2. identify possible associations of these domains with technologic devices use. Methods: Participants were 24 children with leukemia (13 girls), with an average age of 6.54 years (SD = 2.24), recruited at the Pediatric Hematology, Oncology and Stem Cell Transplant Center (University of Padua). They are Caucasian (58.3%) and non-Caucasian Italian speaking (41.7%). Children were assessed with the NEPSY-II theory of mind (TOM) and the Affective Recognition (AR) tests. CBCL and a technological devices use questionnaires were filled in by parents. Results: Comparing with standardized norms, 25% of these patients attested at a sub-clinical level for TOM and 12.5% for AR, while 29.16% attested at a clinical level for TOM and 50% for AR. In nine children 3-5 years old parents recognized sleep and withdrawal symptoms at a clinical (10.2%) or a subclinical (88.8%) level scores. The remaining children aged 6-10 years showed clinically internalizing (69.23%) and externalising symptoms (30.76%). Pre-school children obtained higher scores than the older ones both in TOM (U = 29; p = 0.01) and in AE (U = 34; p = 0.02) tasks. Spearman bivariate correlations identified a significative negative association between time of tablet use and AE score (rho = -0.61; p = 0.001). Children's time of PC use was significantly associated with their internalization symptoms (rho = 0.75; p= 0.03) in pre-schooler and externalization symptoms (rho = 0,62; p = 0,02) in schooler. Conclusions: Specific emotive training should be implemented for more at risk children improving the social bounds between children.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.830 | 0.706 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".