Totally All Alone with My Thoughts: Development, Psychometric Properties and Correlates of the Loneliness Automatic Thoughts Questionnaire
Bibliographic record
Abstract
Introduction: The current article introduces the Loneliness Automatic Thoughts Questionnaire (LATQ) and describes research evaluating its psychometric properties and correlates. Methods: Two separate samples of university student participants (Study 1; N = 282, Study 2; N = 289) were administered the LATQ along with a battery of other measures. Whereas Study 1 involved a preliminary investigation of the psychometric properties of the LATQ, Study 2 provided an opportunity to further expand on this aim by assessing the concurrent validity of the measure across studies. Results: Overall, psychometric analyses confirmed that the LATQ items are measured with an adequate degree of internal consistency and confirmatory factor analyses established that the nine items loaded significantly on one replicable factor. Concurrent validity was established in terms of links with other loneliness measures and a measure of persistent and intrusive negative thoughts. Furthermore, LATQ scores were associated with anti-mattering, social hopelessness, anxiety, depression, and unbearable psychache. Moreover, regression analyses established that the LATQ predicted significant unique variance in depression and psychache beyond the variance attributable to measures of loneliness and adaptability to loneliness. Discussion: Collectively, results indicate that loneliness-related automatic thoughts represent a unique and important element of the loneliness construct. Future research applications and additional psychometric issues to address in future research are discussed and a need for a greater focus on the cognitive aspects of loneliness is explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".