Predicting Loneliness from Where and What People Do
Bibliographic record
Abstract
The many devastating mental health outcomes associated with chronic loneliness is the motivation behind research into examining personal and demographic characteristics of the lonely. The present study sought to examine the connection of where people live (degree of urbanization) and what people do (leisure activities) with self-report of loneliness in a large sample (N = 8356) of unrelated Dutch adults. Information regarding where people live and what they do in their leisure time was entered into a regression analysis for self-reported loneliness. The overall regression was significant and accounted for 2.8% of the loneliness scale scores. Significant independent predictors for loneliness were living in heavily urbanized areas and engaging in fewer social activities. People who went sightseeing or to amusement parks/zoos or who participated in clubs reported being less lonely. Spending time using a computer predicted higher self-report loneliness scores. Consistent with previous research, after controlling for other variables, gender was not a significant predictor of loneliness but both a younger age and a curvilinear or U-shaped curve of age predicted loneliness (the younger and the much older). The results suggest that meaningful interpersonal interactions may result in lower feelings of loneliness.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".