Quality of Life and Associated Factors among Cancer Patients Receiving Chemotherapy during the COVID-19 Pandemic in Thailand
Bibliographic record
Abstract
The dynamics of the COVID-19 pandemic have significantly changed since its initial outbreak. This study aimed to investigate the quality of life (QoL) of patients with cancer receiving chemotherapy in the specific context of Thailand during the COVID-19 pandemic. A cross-sectional study was conducted with 415 patients with cancer. Instruments used were a demographic and clinical characteristics form, the Edmonton Symptom Assessment Scale (cancer symptom burden), Strategies Used by People to Promote Health (self-care self-efficacy), and a Thai version of the Brief Form of the WHO Quality of Life Assessment. Data were analyzed using descriptive and inferential statistics. The participants had an average age of 56 years. They reported a moderate level of QoL across all domains and for the overall QoL during the pandemic. The results of the multiple linear regression model indicated that positive self-care self-efficacy, being married, having health insurance, stage of chemotherapy, and reduced cancer symptom burden were significant predictors of overall QoL (adjusted R2 = 0.4940). Positive self-care self-efficacy also emerged as a primary predictor, positively influencing all QoL domains and overall QoL (p < 0.001). These findings emphasize the significance of self-care self-efficacy in enhancing the QoL of patients with cancer undergoing chemotherapy during the pandemic. Integrating interventions to bolster self-care self-efficacy into the care plans for these patients can help them manage their symptoms, cope with the side effects of cancer treatment, and enhance their overall well-being.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".