Association of using AI tools for personal conversation with social disconnectedness outcomes
Bibliographic record
Abstract
Abstract Aim To examine the association of using AI tools exclusively for personal conversation with loneliness and perceived social isolation and social withdrawal. Subjects and methods We used data from a quota-based online sample consisting of 3270 individuals reflecting the general adult population in Germany aged 18 to 74 years. Psychometrically sound tools were used to quantify the outcomes. Results Adjusting for a wide array of covariates, regressions showed that compared to individuals never using AI tools for personal conversation, individuals using AI tools 1–3 times a month or less often for personal conversation mostly reported somewhat poorer social disconnectedness outcomes. Individuals using AI tools at least once a week for personal conversation, showed markedly poorer social disconnectedness outcomes (compared to never-users). Such associations were particularly pronounced among men and younger individuals. Conclusion Frequent use of AI tools exclusively for personal conversation is associated with social disconnectedness outcomes. Our present study provides the first insights into the relationship between AI tools for personal conversation and poorer social disconnectedness outcomes, laying the groundwork for future research in this field.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".