The Translation and Preliminary Psychometric Validation of the Ghosting Questionnaire in Urdu
Bibliographic record
Abstract
BACKGROUND: "Ghosting" refers to the practice of abruptly cutting off all contact with a person with whom you have been in constant correspondence. The break comes without warning and without understandable provocation. The term most commonly applies to online romantic relationships. The motives for and effects of ghosting have been studied, and validated research questionnaires have been developed; however, there are no such questionnaires available for Urdu speakers. The purpose of this study was to adapt the "Ghosting Questionnaire (GQ)" for use in Pakistan and India, two of the world's most populous countries-a process that involves translation, adaptation, and validation. METHODS: The study's methodology involved translating the GQ into Urdu using both forward and backward translation techniques. Convergent validity, test-retest reliability, internal consistency, confirmatory factor analysis, and goodness of fit were all components of the psychometric analyses. CONCLUSIONS: The Urdu version of the GQ demonstrated a good internal consistency, with the Cronbach's alpha and McDonald's omega both exceeding 0.90. It also showed a high test-retest reliability-(0.96). The one-factor structure was confirmed by the confirmatory factor analysis, which agreed with the original English version of the GQ.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".