Systematic Review on Factors Associated With Self-perceived Burden Among Cancer Patients
Bibliographic record
Abstract
Abstract Introduction: Cancer is the leading cause of death in the world. There was a high prevalence of high self-perceived burden (SPB) among cancer patients and this could bring adverse consequences on the physical and mental health of cancer patients, which can lead to suicide if not treated well. This review aims to determine the prevalence of SPB among cancer patients and its risk factors.Methods: Published journals before September 2021, from five databases (PubMed, ScienceDirect, Springer, Cochrane, and CNKI) were be retrieved according to the keywords. The keywords used included cancer patients, terminally ill patients, cancer, SPB, self-perceived burden, self-burden, self-perceived, factor, predictor, associated factor, determinants, risk factor, prognostic factor, covariate, independent variable and variable. The quality of the inclusion and exclusion criteria was independently reviewed by three researchers. Results: Out of 12712 articles, there are 22 studies met the eligibility criteria. The prevalence of SPB among cancer patients ranged from 73.2% to 100% from Malaysia, China, Canada. Most of them had moderate SPB. Out of the reported factors, age, gender, marital status, ethnicity, residence, educational level, occupational status, family income, primary caregiver, payment methods, disease-related factors, psychological factors and physical factors were mostly reported across the studies.Conclusions: In conclusion, SPB prevalence is high in cancer patients. Therefore, hospitals, non-governmental organizations, relevant policy makers and communities can provide special programs for high-risk groups to provide psychological guidance or design corresponding interventions to reduce the SPB level of patients and improve the quality of life.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".