Study of relationship between elderly people's alexithymia and mind-reading
Bibliographic record
Abstract
Objective To study of relationship between elderly people's alexithymia and mind-reading. Methods There were 108 old people over 70 years old, tested by mind-reading,cognitive ability and twenty-item Toronto alexithymia scale. Results There were significant difference in mind-reading(16.00±4.84, 19.36±2.91,t =3.38,P ＜0.01), memory span(5.80±1.42,7.18±1.13,t =4.28,P ＜0.01), working memory( t =4.05,P ＜0.01),processing speed( t =-4.80,P ＜0.01) between high and low alexithymia groups.Alexithymia scores were negatively related to the scores of mind-reading, memory span,working memory,processing speed,correlation coefficient was 0.227( P <0.05),-0.377( P <0.01),-0.334( P <0.01),-0.470( P <0.01),respectively. With mind-reading,cognitive processing and educational level as independent variables,with total scores, F1, F2, and F3 of TAS as dependent variable,respectively,the regression equation of total scores was significant( F =8.594,P ＜0.01), and the one of F1 was significant( F =8.796,P ＜0.01),but the one of F2( F =1.735,P ＞0.05) and F3( F =1.181,P ＞0.05) was not significant. Conclusion Elderly people's alexithymia is negatively related to mind-reading and several cognitive abilities. Mind-reading could affect emotional recognition, but not affect emotional description and export-oriented thinking. Key words: Old people; Alexithymia; Mind-reading
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".