Cross-cultural differences in alexithymia and interoception between the UK and Singapore
Bibliographic record
Abstract
Although limited, initial studies exploring alexithymia cross culturally suggest that Asian cultures report higher levels of alexithymia when compared to European-American cultures (e.g. Aival-Naveh et al., 2019; Le et al., 2002). In addition, some differences have arisen within the research when examining specific aspects of alexithymia instead of the construct as a whole. For example, Dere et al. (2012) only found differences in the externally oriented thinking subscale scores. As a result, continued research is required that examines the total and subscale scores of alexithymia between cultures. A closely linked construct to alexithymia is interoception. Cross culture research into the differences in interoception is also limited but it is suggested that Asian cultures have a greater awareness of their bodily sensations compared to more Western cultures (e.g. European and American cultures). Primarily this study is exploratory. We aim to examine the differences in levels of alexithymia and interoception across Asian (Singaporean) and European (UK) samples. We also aim to explore the possible culture differences in value of expression, social media use, body image and eating behaviours.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".