Social Anxiety in University Students: Towards an Intentional Life-Skills Based Prevention Model
Bibliographic record
Abstract
Research suggests that staying connected with people is very beneficial to our physical and mental well-being. Moreover, a lack of social connection is associated with poor mental and physical health, and lower overall well-being. For individuals with social anxiety, it is particularly difficult to cultivate social connections. Due to the prolonged period of social isolation during the COVID-19 pandemic, research suggests that social anxiety in university students has increased. This study employed a convergent parallel mixed method design and administered a self-reported questionnaire which included quantitative and qualitative questions. The questionnaire was administered to 301 undergraduate students to determine if feelings of social anxiety in students changed during and after the pandemic. This study also analyzed social anxiety levels across racial and ethnocultural demographics and assessed the cultural stigmas and barriers that may prevent students from accessing mental health services. Results from the quantitative analyses showed a significant difference in social anxiety scores before and after the pandemic. However, in our sample, feelings of social anxiety post-pandemic did not differ across race, or income which were our main variables of interest. In addition, there was a positive correlation between social anxiety scores and household income and fear of negative evaluation. The qualitative results showed that important barriers to accessing mental health services include fear of parents learning they are in therapy, cost of mental health services, language barriers, and concern that a therapist would not have cultural sensitivity. This study highlights the need for increased interventions to reduce social anxiety among students, and proposes a preventative approach we refer to as “Life-Skills Training” to address social anxiety.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".