Factors effecting quality of life for family caregivers of cancer patients in Kenya
Bibliographic record
Abstract
Background: Characteristics, including age, educational level, economics, and geographical setting during care provision significantly affect quality of life (QoL) among cancer patient caregivers in high-income countries. Investigation in middle/low income countries is limited. Objective: To explore the factors associated with QoL among family caregivers (FCG's) of cancer patients in Kenya. Methodology: This was a correlational study conducted at the largest teaching and referral hospital in Kenya. The study enrolled 164 family caregivers of cancer patients. The QoL (Family Version) was used to measure Quality of Life. Data collection was done using interviewer-administered questionnaires. A student t-test and Pearson chi-square were used to determine the association between personal, social, and disease characteristics and family caregiver quality of life. Results: The average mean score of family caregiver QoL was 55.8 (SD±10.12) percent, which is lower than in other countries. Conclusion: There was a significant association between family caregiver quality of life (QoL) and level of education, relationship to the patient, caregivers' ability to carry out normal activities, and caregiver knowledge of the stage of 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".