The relationship between loneliness and school dropout
Bibliographic record
Abstract
• Examined loneliness and interpersonal relationships in thinking about dropping out of school. • Asked about dropping out of high school and first year university in a sample of university students. • Loneliness was the strongest predictor for the dropping out of high school and university questions. Previous research has focused on demography, social factors, and school achievement as determinants of school dropout. The present study looks deeper into the subjective experiences of students, with a particular interest in the roles of loneliness and interpersonal relationships in school dropout. A questionnaire was completed by undergraduate university students (255 men, 246 women, 1 other), containing a measure of loneliness, as well as questions concerning friendship, social interaction, and thoughts of dropping out of high school and first year university. Results showed that loneliness was a strong predictor for the dropping out of high school and university questions, increasing the odds by two and a half times. In addition, thinking about dropping out of high school significantly predicted thoughts about dropping out of university. Although this study examined thoughts about leaving school and not actually dropping out, the results do demonstrate how loneliness is negatively impacting students.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".