Multiple Informant Agreement of Child, Parent, and Teacher Ratings of Child Anxiety within Community Samples
Bibliographic record
Abstract
OBJECTIVE: Extant research concerning the degree of multiple informant (that is, parent, clinician, teacher, and child) agreement for child anxiety ratings generally uses clinical samples, and results have been mixed. METHOD: Our study used a community sample of public school children (n = 1039) to investigate child (self), parent, and teacher reports of child anxiety across 3 time points (pretreatement, posttreatment, and follow-up) in 3 independent school prevention and intervention trials. RESULTS: Results showed that parents and teachers had high informant agreement for ratings on anxiety across the 3 time points (r = 0.95 to 0.96, P < 0.001); agreement between parent and child (self) reports and between teacher and child (self) reports consistently showed lower agreement across the 3 time points (r = 0.14 and 0.28, respectively, P < 0.001). Group differences were also significant for sex and grade, whereby females more commonly self-reported higher anxiety and children in grades 3 and 4 self-reported higher anxiety, compared with students in grades 5 to 7. CONCLUSION: Correlations between parent and teacher with child ratings were poor over 3 time points, and significant differences were found for sex and grade. Research is needed to understand reasons for poor concordance between parent, child, and teacher ratings of anxiety for all children.
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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".