Childhood language skills and adolescent self-esteem in preterm survivors
Bibliographic record
Abstract
Fifty-seven percent of children with poor language skills are affected by socio-emotional problems. Despite the importance of language skills to interpersonal functioning and school performance, relatively little is known about how they affect self-esteem in adolescence. Data on youth at high risk for language problems (e.g. those born extremely low birth weight (ELBW; <1000 grams)) are even more scarce. This prospective study examined associations between language skills at age 8 and self-esteem during adolescence (12-16 years) in individuals born at ELBW ( N = 138) or normal birth weight (NBW; >2500 grams) ( N = 111). Participants' language skills were assessed using the Verbal Scale of the Wechsler Intelligence Scale for Children-Revised and the Token Test at age 8. In adolescence, participants completed the Harter Self-Perception Profile for Adolescents. Birth weight status was found to moderate associations between childhood language and adolescent global self-esteem (Token Test ( p = .006), Verbal Intelligence Quotient ( p = .033)) such that better language skills were associated with higher self-esteem in adolescent ELBW survivors, but not in NBW participants. Language skills may play a protective role in the development and maintenance of self-esteem in ELBW youth and could be an important target for optimizing their functioning, particularly before transitioning to the critical adolescent period.
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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) | 0.000 | 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 | 0.000 |
| 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".