Palliative Care Knowledge and Attitudes Among Oncology Nurses in Qatar
Bibliographic record
Abstract
OBJECTIVES: Formal palliative care (PC) education is lacking in the middle eastern state of Qatar. This study was done to assess the need for PC education among oncology nurses in Qatar. METHODS: In March 2012, a self-constructed questionnaire was distributed to 115 nurses at the Qatar National Center for Cancer Care and Research. RESULTS: A total of 115 nurses responded to the questionnaire. The majority (87.8%) were female. Although 60% had more than 10 years of work experience, only 31% had received formal training in PC, with only 6.1% having completed postgraduate training. The majority (63%) of responders attributed this issue to unavailability of PC courses rather than lack of time, interest, or financial issues. Currently, only 16.7% did not express interest in the field, with 56% showing some kind of interest. In terms of knowledge, 54% of the responders were familiar with the World Health Organization ladder for pain relief. Only 43.6% know about Palliative Performance Scale, and half of the nurses know the Edmonton Symptom Assessment System. Overall, 56% of the nurses indicated a need for training in more than 1 aspect. These aspects included training in care of the dying patients (14.6%), communication strategies (22%), caregiver support (10.6%), psychosocial care (15%), pain management (10.2%), other symptom management (13%), and other ethical/spiritual issues (14.2%). CONCLUSIONS: There is a clear deficiency in formal PC education among the nurses at the National Center for Cancer Care and Research, in Qatar. This is reflected by their lack of experience and exposure to PC and their mediocre knowledge in the field. This could be attributed to the fact that formal PC service was established only recently in Qatar (2008). Formal training courses in PC nursing are required.
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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 | 0.003 |
| 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 | 0.000 |
| 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".