Exploring psychological distress among lung cancer patients through the stress system model
Bibliographic record
Abstract
Lung cancer, a leading cause of cancer-related mortality globally, often leads to anxiety, fear and other psychological distress due to its poor prognosis, treatment challenges, and financial burden. Prolonged distress may progress to depression or other mental health disorders, adversely affecting patients' quality of life and treatment outcomes. This study examines the prevalence and determinants of psychological distress in lung cancer patients, offering a theoretical basis for timely clinical interventions. This cross-sectional study applied the stress system model to analyze 435 conveniently sampled lung cancer patients in three Chinese tertiary hospitals from September 2023 to February 2024. Data were collected using the Distress Thermometer (DT), Medical Coping Modes Questionnaire (MCMQ), Brief Illness Perception Questionnaire (BIPQ), Edmonton Symptom Assessment Scale (ESAS), Perceived Social Support Scale (PSSS), and Type D Personality Scale (DS14). Statistical analysis, conducted with SPSS 25.0, utilized univariate, correlation, and binary logistic regression analyses to systematically explore the interrelationships and influence mechanisms of these factors on psychological distress from a holistic stress system model perspective. Among 435 lung cancer patients, 52.87% experienced psychological distress (DT = 4.24 ± 2.356). Significant risk factors (P < 0.05) included age, occupational status, family monthly income, payment method, avoidance coping style, symptoms and type D personality. As the detection rate of psychological distress in lung cancer patients is high, clinical staff should dynamically observe the psychological changes of lung cancer patients, do a good job in screening and stratified management of psychological distress, and provide interpersonal psychological guidance to establish a positive mindset, so as to reduce the negative emotions of patients, and to improve the quality of life of patients' health-related issues.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".