Assessing the validity of three measures of loneliness through correlations and nomological networks
Bibliographic record
Abstract
Examining loneliness with self-reports can be done succinctly with a single item, briefly using a few items, or more extensively with longer scales. The present study examines three loneliness measures: a single item, a three-item scale, and a 20-item scale. Participants ( N = 407) completed the three measures of loneliness, a personality inventory measuring six dimensions, and provided demographic information of age and sex. With respect to convergent validity, there were high correlations (between 0.49 and 0.69) among the three loneliness measures. Intraclass correlations demonstrated stronger profile similarity with demographic variables and personality dimensions for the three-item and 20-item loneliness measures compared to the single item. We conclude that each loneliness measure does assess a similar construct but caution using the single item only because of unknown reliability in cross-sectional studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".