Cognitive - emotional reeducation in case of anxiety about exams
Bibliographic record
Abstract
The problem of test anxiety occurs in all levels of education, from elementary school to higher education; school failure is related to this affective disorder, present in absenteeism, failure, low grades and dropout. In a study by Menéndez (2004), 30% of school failure is due to emotional problems, including anxiety, triggered by the personal situation of the student in front of the conception of academic demands. Anxiety can increase or decrease depending on the type of material; for example, it is reported in the PISA 2012 report, that of 10 Mexican students studying a subject suffering from math anxiety 8; 10 students and 5 expressed anxiety at the time of solving a problem. Of the OECD countries, Mexico is the country with the highest math anxiety (OECD, 2013) In the specific case of higher education in Mexico, Hernández-Pozo (2008), cites data from INEGI 2004, two million students who entered higher level, around a quarter of potentially manifested anxiety disorders. In a study by Vargas (2010), a vicious circle of performance and anxiety was found in the time of testing, the lower is the greatest achievement is the rate of anxiety, if it is to increase the academic achievement is higher prevalence anxiety and anxiety is increasing lower 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".