Effects of cancer severity on the relationship between emotional intelligence, perceived social support, and psychological distress in Italian women
Bibliographic record
Abstract
PURPOSE: This study aims to understand the association between emotional intelligence, perceived social support, and psychological distress (i.e., anxiety, depression, stress) in women with cancer at different stages. Specifically, the aims of this study were to investigate: i) the links between emotional intelligence and psychological distress (i.e., symptoms of anxiety, stress and depression); ii) the mediating role of perceived social support provided by family members, friends, and significant others in the relationship between emotional intelligence and psychological distress; iii) the impact of cancer type and cancer stage (I-II vs III-IV) in moderating these relationships, among Italian women. METHODS: The research sample consisted of 206 Italian women (mean age = 49.30 ± 10.98 years; 55% breast cancer patients) who were administered a questionnaire to assess emotional intelligence, perceived social support, and psychological distress. Structural equation model (SEM) analysis was carried out to confirm the hypothetical-theoretical model. RESULTS: Emotional intelligence had a positive association with perceived social support, which in turn prevented psychological distress only in women with early-stages cancers. The type of cancer has no effect on these relationships. CONCLUSIONS: The findings of this study indicate a pressing need to screen and recognize women with lower emotional intelligence and perceived social support, as they may be more prone to experiencing psychological distress. For such individuals, our results recommend the implementation of psychological interventions aimed at enhancing emotional intelligence and fortifying their social support networks, with consideration for the stage of cancer they are facing.
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 | 0.001 |
| 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".