A cross-sectional study on associations of physical symptoms, health self-efficacy and suicidal ideation among Chinese hospitalized cancer patients following diagnosis.
Bibliographic record
Abstract
Abstract Background: To examine the association between physical symptoms and suicidal ideation among Chinese hospitalized cancer patients post-diagnosis, and test the modifying effect of health self-efficacy on this association. Methods: A cross-sectional study was conducted among 544 cancer patients from oncology setting in two general hospitals in northeastern China. Suicidal ideation data was collected by face-to-face interview using the Yale Evaluation of Suicidality scale (YES). Patients also rated on the McGill Quality of Life Questionnaire (MQOL), the Hamilton Depression Rating scale (HAMD-17) and the Strategies Used by People to Promote Health scale (SUPPH). Multivariable logistic regression was applied to examine the impact of physical symptoms, health self-efficacy and their interactions on suicidal ideation. Results: We found a suicidal ideation rate of 26.3% in patients following cancer diagnosis. Logistic regression showed that insomnia (aOR=1.84, 95% CI 1.13 to 3.00, p =0.015), lack of appetite (aOR=2.14, 95% CI 1.26 to 3.64, p =0.005) were significantly associated with suicidal ideation, low health self-efficacy showed a marginally significant exaggerating effect on the association between pain and suicidal ideation (aOR = 2.77, 95% CI 0.99 to 7.74, p =0.053), even after controlling socio-demographic, clinical characteristics and depression. Conclusions: Insomnia, loss of appetite, even after adjusting depression, are associated with suicidal ideation, health self-efficacy play a moderating role on pain and suicidal ideation among Chinese cancer patients. Paying attention to these physical symptoms and promoting the sense of health self-efficacy could be useful for suicide intervention.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".