Positive personal resources and psychological distress during the COVID-19 pandemic: resilience, optimism, hope, courage, trait mindfulness, and self-efficacy in breast cancer patients and survivors
Bibliographic record
Abstract
PURPOSE: This study aims to understand the association between positive personal resources (i.e., optimism, hope, courage, trait mindfulness, and self-efficacy), resilience, and psychological distress (i.e., anxiety, depression, stress) in women with breast cancer and breast cancer survivors during the COVID-19 pandemic. We hypothesized that personal positive resources can directly influence resilience, which in turn prevented psychological distress. METHODS: The research sample consisted of 409 Italian women (49% patients, 51% survivors) who were administered a questionnaire to assess positive resources, resiliency, and distress. structural equation model (SEM) analysis was carried out to confirm the hypothetical-theoretical model. RESULTS: Personal positive resources had a direct positive effect on resilience, which prevented from distress. These results were observed across cancer patients and survivors, and regardless the level of direct exposure to COVID-19. CONCLUSIONS: In both patients and survivors, the relationships between positive personal resources, resilience, and psychological distress is strong enough to be not influenced by the level of exposure to COVID-19 and despite COVID-19 pandemic caused the disruption of active treatment plans and delays in routine check-ups. IMPLICATIONS FOR CANCER SURVIVORS: Implications of this study suggest the urgency to screen positive resources and to identify women with lower resilience and a potentially higher susceptibility to develop psychological distress. For these women, our findings suggest the implementation of psychological interventions that build resilience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".