Latent Structure and Item Functioning of Self-Referent Encoding Task Word Stimuli in Preadolescent Youth
Bibliographic record
Abstract
The Self-Referent Encoding Task (SRET) can be used to measure self-concept via endorsement of trait words, a robust metric associated with depression severity. Our study is the first to investigate the structural validity and item functioning of SRET endorsement scores using confirmatory factor analysis and item response theory. Community-dwelling preadolescent youth ( N = 508; M age = 12.39 years, SD age = .72) were shown a list of positive and negative trait adjectives and made binary ratings of whether words were self-descriptive. The SRET exhibited a two-factor structure, comprising positive and negative factors. Positive items were endorsed by most children and best estimated information about positive self-concepts below average levels of positivity. Conversely, negative items were unendorsed by most children and best estimated information about negative self-concepts above average levels of negativity. We identify standardized, psychometrically sound, and developmentally sensitive SRET items for assessing youth self-concept and its associations with depression risk.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".