The practice of palliative care clinical nurse specialists in the care of cancer patients in the State of Qatar
Bibliographic record
Abstract
Background and objective: There is an annual increase in the number of metastatic advanced cancer patients who require palliative care; this is paralleled by the necessary increase in the workload of palliative care Clinical Nurse Specialist (CNS). Palliative care CNSs are crucial members in the palliative care multidisciplinary team. The aim of this paper is to describe the role of clinical nurse specialists by clarifying the ambiguity of their responsibilities in advanced cancer patients' care in the state of Qatar.Methods: Data used in this descriptive study were extracted from a shared Excel data registry system that has been created by palliative care CNSs. Data were retrieved from the 1st of January 2021 till the 31st of December 2021 and translated into numbers and percentages. Results: A total of 3,571 patients' encounters were captured starting from the 1st of January 2021 till the 31st of December 2021. National Center for Cancer Care and Research (NCCCR) inpatient's encounters (33%), followed by telephone consultations (32%) were the highest two approaches to palliative care services delivered by CNSs. Pain and non-pain symptoms management, early introduction to palliative care, patient and family education and support, end-of-life care, and coordination of care with other health care professionals are few examples of the other services offered by palliative care CNSs for cancer patients at Hamad Medical Corporation (HMC).Conclusions: Palliative care CNSs’ roles are multidimensional and highly needed. Some of their clinical activities are unnoticed, especially due to the high number of patients’ encounters and the different geographic locations at HMC that CNSs cover. Future research is highly needed to describe the role of palliative care CNSs in areas, such as research, education, and leadership.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".