Prevalence and Associated Factors of Anxiety and Depression in Lung Cancer Patients
Bibliographic record
Abstract
PURPOSE: Given the prevalence of anxiety and depression amongst lung cancer patients in China, it is critical to identify potential factors contributing to these symptoms to improve future treatment strategies. However, current research has primarily focused on clinical variables, leaving various sociodemographic factors largely underexplored. Examining these aspects is essential for enhancing clinical interventions and patient care, as sociodemographic factors can significantly influence psychological outcomes. METHODS: A total of 486 lung cancer patients were included in the study. Data on anxiety and depression were collected using the Hospital Anxiety and Depression Scale (HADS), and sociodemographic information was gathered via a structured questionnaire. Clinical data was retrieved from the hospital's database. Univariate analysis and multivariate logistic regression were applied to identify sociodemographic and clinical factors that were significantly associated with anxiety and depression. RESULTS: The findings revealed prevalence rates of 24.07% for anxiety and 25.72% for depression. Local residency in Shanghai, internet use, financial strain, and advanced cancer stages (III or IV) were associated with a higher level of both anxiety and depression. Having a university/college education or higher was solely linked with increased anxiety levels. CONCLUSION: Local residency, internet use, financial strain, cancer stage, and educational background are key predictors of anxiety and depression amongst lung cancer patients. It is crucial for healthcare professionals to monitor and support the mental well-being of lung cancer patients, especially those affected by these identified factors.
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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 | 0.000 |
| 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".