The subject matters: relations among types of anxiety, ADHD symptoms, math performance, and literacy performance
Bibliographic record
Abstract
Sabrina M. Di Lonardo Burra* & Jo-Anne LeFevreab a Department of Cognitive Science, Carleton University, Ottawa, Canadab Department of Psychology, Carleton University, Ottawa, CanadaCONTACT Sabrina M. Di Lonardo Burr sabrina.dilonardo@carleton.ca; sabrinadilonardo@cmail.carleton.ca Department of Cognitive Science, Carleton University, Ottawa, CanadaSupplemental data for this article can be accessed http://doi.org/10.1080/02699931.2021.1955243.ABSTRACTIn this study, we extended a model of the relations among ADHD symptoms, anxiety, and academic performance. Undergraduates (N = 515) completed self-report measures of anxiety (i.e. trait, test, math, and literacy) and ADHD symptoms. During the study, they completed math and literacy measures and reported their current (i.e. state) anxiety three times. There were significant correlations among ADHD symptoms and all measures of anxiety. However, neither ADHD symptoms nor trait anxiety were correlated with math or literacy performance. Test-anxious students reported increasing state anxiety as the study progressed. In contrast, math-anxious students reported increased state anxiety following the math tasks but not the literacy tasks and literacy-anxious students reported increased state anxiety following the literacy tasks but not the math tasks. With respect to performance, math-anxious students had worse math performance and literacy-anxious students had worse literacy performance whereas test anxiety was not directly related to performance on either math or literacy tasks. Patterns of relations did not differ for men and women, but, except for literacy anxiety, women reported higher levels of all types of anxiety. These results could help educators understand how affective responses are related to university students’ academic performance.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.004 |
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".