Development and Validation Analysis of Redeemer’s University Alexithymia Scale (RUNAS)
Bibliographic record
Abstract
Background: In Nigeria, alexithymia, “no words for feelings” is understudied and under-assessed despite its significance in physical and psychological health outcomes. This study attempts the development of a standardised alexithymia scale. Methodology: The development of this scale is in two phases: the first phase is the development and refinement of screening tool items and the second phase establishes the scale’s psychometric properties. Results: The observed KMO measure of sampling adequacy is .59 with a significant Bartlett’s test of sphericity (X2 = 1022.608, df = 561, p = .000). The test of the principal components indicated twelve components extracted. Based on Principal Component Analysis, only 12 items in one component were found significant and retained as part of the final scale. The item-total statistics and Cronbach coefficient (α) of .79, a Spearman-Brown coefficient of .80, and Guttman Split-Half coefficient of .79 of the tool indicate that all items have good discrimination and should be retained. The internal consistency of RUN-PDST among the Nigerian sample revealed that the screening tool is reliable. Paired with TAS-20, RUNAS has good concurrent validity. Conclusion: RUNAS has appropriate psychometric properties for assessing alexithymia in Nigeria and similar cultural contexts.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".