A bibliometric analysis on adolescent social anxiety and psychoeducational variables in Web of Science 2002–2021
Bibliographic record
Abstract
The aim of this bibliometric analysis was to analyze the scientific output on adolescent social anxiety and its relationship with 15 psychoeducational variables in peer-reviewed journals during the period 2002–2021. The goal was to provide a comprehensive overview of the state of the art on adolescent social anxiety and academic/school achievement, performance, self-concept, self-esteem, self-efficacy, self-attributions, goals, attachment, adjustment, engagement, refusal, absenteeism, anxiety, learning strategies, and self-regulated learning. A search of scientific literature was conducted using Web of Science, and 157 empirical studies were identified. Analyses were conducted using bibliometrix 3.1 to avoid the risk of bias. The results suggested progressive growth in the scientific output on this research topic mainly in the USA, China, Spain, and Canada, and revealed trending issues and scientific interest regarding the relationship between adolescent social anxiety and academic/school achievement and performance. Other variables, such as academic/school attachment and self-regulated learning did not emerge. The results provide implications for practitioners (i.e., educators, clinical and educational psychologists, and psychiatrists), supporting emerging lines of research. Limitations include a lack of a review protocol and a lack of comparison with other international databases, such as PsychInfo, Scopus, PubMed, or ERIC.
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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 | 0.015 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.284 | 0.347 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".