Knowledge and Practice of Oncology Social Workers in the Management of Cancer Patients at UNTH Enugu, Enugu State, Nigeria
Bibliographic record
Abstract
The increasing number of patients living with cancer as a chronic disease stresses the importance of paying attention to rehabilitative and psychosocial care which is the major role of oncology social workers. The study aimed at appraising the knowledge and practice of oncology social workers among cancer patients in UNTH. The study adopted the questionnaire and in-depth interview guide in generating data to answer research questions and test hypotheses. Responses to questionnaire were elicited from 574 respondents while the in-depth interview had 10 participants. Responses to questionnaire were analyzed quantitatively using SPSS version 20, while those for in-depth interview were analyzed thematically to complement the quantitative data. Hypotheses were tested using Chi-square (χ2), while the long-run significance of certain predictor variables on the dependent variable was ascertained using binary logistic regression. Findings from the study revealed that majority of respondents had no knowledge of oncology social work. Place of residence and level of education had significant relationship with knowledge of oncology social work at (p<0.000) and (p<0.038) respectively, while patient status had no significant relationship with benefits from oncology social work services; they were all confirmed in the binary logistic regression analysis except level of education. Given the poor knowledge of oncology social work among cancer patients, the study established a case for social work. Therefore, specific programmes like outreach programmes and enlightenment campaigns on the importance of oncology social work for cancer patients of all socio-economic background as well as those with lower level of education, their families, health practitioners among others should be made available with the aim of improving their knowledge and accessibility of oncology social work.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".