[Alexithymia and negative emotions in cancer patients: Mediating effects of intrusive thoughts].
Bibliographic record
Abstract
OBJECTIVE: To explore associations of negative emotions with alexithymia and intrusive thoughts in cancer patients. Methods: A total of 115 cancer patients were assessed by Impact of Event Scale-Revised, the 20-item Toronto Alexithymia Scale, the Center for Epidemiological Studies Depression Scale, and the state anxiety subscales of State-Trait Anxiety Inventory. Results: Negative emotions were positively correlated with alexithymia and the intrusive thoughts (r 0.251 to 0.600, P<0.01). Intrusive thoughts were significantly associated with the total score of alexithymia, difficulty in identifying feelings, and difficulty in describing feelings (r 0.261 to 0.430, P<0.01). The relation between alexithymia and negative emotions was partially mediated by intrusive thoughts, accounting for 40.71% of the alexithymia in total negative emotions. Conclusion: Intrusive thoughts play a role, at least partially in mediation of alexithymia and negative emotions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".