Confirming The One-Item Question Likert Scale To Measure Anxiety
Bibliographic record
Abstract
Background: A previous study has confirmed the feasibility of one-item question scales to adequately measure current anxiety in a female-only sample in clinical settings. This study aims to further determine whether a one-question likert scale can be used in measuring anxiety in the general population for both genders. Method: A convenience sample of adults, recruited via a smartphone application (app) published freely in Apple’s app store that have completed the Social Phobia Inventory (SPIN) questionnaire and a one-item anxiety question scale. Result: 1,233 participants from the US, UK, Australia, and Canada completed the questionnaire. 67% of participants were female, with a high school education level, and a relatively young age, while the participants’ mean age was 28.7. Chi-square analysis has shown no significant differences between countries, in terms of gender χ2 (3, N = 1233) = 4.5 p = .21, education level χ2 (9, N = 1233) = 14.5 p = .10, and age F(3,1232) = 1.52, p = .21. There was a strong, positive, partial correlation between SPIN score and the one-question anxiety scale, controlling for age, gender, country, and education, r = .72, p < .001, with a high SPIN score being associated with a higher score on the one-question anxiety scale. An inspection of the zero order correlation (r =.73) suggested that age, gender, country, and education had very little effect on the strength of the relationship between SPIN score and the one-question anxiety scale. Conclusion: This study confirmed that the one-question anxiety scale is suitable to be used to measure anxiety in both genders.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".