Caregivers satisfaction with care received by paediatric oncology patients admitted at the University Teaching Hospital-Lusaka Zambia
Bibliographic record
Abstract
Background: Diagnosis of cancer is a traumatic event that does not only affect the diagnosed patients but also their caregivers. Supportive interventions are therefore essential for the caregivers to go through the cancer pathway and attain a sense of satisfaction for both medical and none medical services. Therefore evaluating the extent to which patients and their caregivers are satisfied with health services is clinically relevant as satisfied patients are more likely to comply with treatment, take an active role in their own care, continue using medical care services and stay under the care of a health provider.Methods: This was a descriptive cross-sectional study that investigated caregiver satisfaction with care received by paediatric oncology patients at the Paediatrics University Teaching Hospital, Lusaka-Zambia. Purposive sampling was used to select study participants and all caregivers who were available in the paediatric oncology ward at the time of data acquisition were selected resulting into a sample size of 50 caregivers. Data was collected using a structured interview schedule.Results: More than half 26 (52%) of the respondents were knowledgeable about cancer disease, 13 (26%) were partially knowledgeable with only 11 (22%) were not knowledgeable. Despite high knowledge levels, 19 (38%) did not know the cancer stage of their child. The majority 30 (60%) of patients had partial access to cancer treatment with only 3 (6%) who reported having full access to treatment. Concerning professional support, majority 47 (94%) received good or very good support from the staff while only 3 (6%) received poor support. When all the factors assumed to influence satisfaction were considered, 18 (36%) of the respondents were very satisfied, 19 (38%) were partially satisfied with only 13 (26%) who were not satisfied with care received. Of the five factors which were hypothesized to influence caregiver satisfaction, only professional support had a significant association with caregiver satisfaction with a p value of .004.Conclusions: Only professional support had a significant association with caregiver satisfaction against all other factors hypothesized to influence satisfaction, implying that if appropriate profession support is given, caregivers are more likely to get satisfied with care and consequently comply with treatment.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".