Competencies for Nurses Regarding Psychosocial Care of Patients With Cancer in Africa: An Imperative for Action
Bibliographic record
Abstract
Psychosocial care is considered an important component of quality cancer care. Individuals treated for cancer can experience biologic or physical, emotional, spiritual, and practical consequences (eg, financial), which have an impact on their quality of living. With the establishment of cancer centers in Africa, there is growing advocacy regarding the need for psychosocial care, given the level of unmet supportive care needs and high emotional distress reported for patients. Nurses are in an ideal position to provide psychosocial care to patients with cancer and their families but must possess relevant knowledge and skills to do so. Across Africa, nurses are challenged in gaining the necessary education for psychosocial cancer care as programs vary in the amount of psychosocial content offered. This perspective article presents competencies regarding psychosocial care for nurses caring for patients with cancer in Africa. The competencies were adapted by expert consensus from existing evidenced-based competencies for oncology nurses. They are offered as a potential basis for educational program planning and curriculum development for cancer nursing in Africa. Recommendations are offered regarding use of these competencies by nursing and cancer program leaders to enhance the quality of care for African patients with cancer and their family members. The strategies emphasize building capacity of nurses to engage in effective delivery of psychosocial care for individuals with cancer and their family members.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".