Demoralization profiles and their association with depression and quality of life in Chinese patients with cancer: A latent class analysis
Bibliographic record
Abstract
Abstract PurposeThe study aimed to identify latent classes of demoralization and examine their association with depression and with quality of life (QOL) among patients with cancer.MethodsCross-sectional data from 874 patients with cancer from three tertiary hospitals in Fujian province were collected using a convenience sampling method. Demoralization, depression, and QOL were assessed using the Chinese version of the Demoralization Scale-II, Patient Health Questionnaire-9, and McGill Quality of Life Questionnaire. Latent class analysis was performed on demoralization profiles. Binary logistic regression and multiple stepwise linear regression were used to examine the identified classes’ associations with depression and QOL.ResultsThree latent classes of demoralization were identified: the “low demoralization and emotional disturbance” class (Class 1; 49.6%); “moderate demoralization and meaninglessness” class (Class 2; 29.1%); and “high demoralization and existential despair” class (Class 3; 21.3%). The severity of depression increased and the levels of QOL decreased with the three classes of demoralization. Patients with cancer in Classes 1 and 2 were 0.128 and 0.018 times more likely to be depressed than those in Class 3, respectively, whereas the magnitudes of decrease in QOL scores for Classes 2 and 3 were 0.378 and 0.629, respectively. ConclusionThis study revealed three heterogeneous classes of demoralization in Chinese patients with cancer and indicated that increased classes were associated with more severe depression and decreased QOL. Targeted, step-by-step psychological interventions should be developed and implemented according to the characteristics of each class of demoralization to effectively promote psychological well-being among patients with cancer.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".