Online schooling as a predictor of loneliness: a cross cultural perspective
Bibliographic record
Abstract
Loneliness has been both theorised with regards to its onset and development and studied with respect to its prevalence among undergraduate students and its effects on cognition and physiology by social scientists both generally and specifically to young adults. Research has studied the prevalence of loneliness and multiple specific effects at an undergraduate level e.g. how loneliness effects each gender. However, the extensive body of literature involves undergraduate students who experience in-person classes. With the mass introduction of online teaching as a response to the covid-19 pandemic, this study aimed to fill the gap in the literature by providing a cross cultural perspective between two countries (Ireland and Canada) as to whether a relationship exists between loneliness and hours spent proportionately both in-person and online. Additionally, type of residence was factored in allowing for a greater and novel understanding as to how that predicts loneliness. Participants (N = 157) were recruited both through social media using voluntary response sampling and through recruitment websites using simple random sampling. Participants shared relevant demographic information and completed the UCLA Loneliness Scale. Results of a multiple linear regression found that in-person hours, online-hours, and the relationship between online hours and in-person was not a significant predictor of loneliness. This study indicates that other factors, such as age and country, are better predictors for loneliness occurring rather than the number of hours spent either with in-person or online schooling. The result of this study allows for a greater perspective into what the possible factors that predict 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".