Bibliographic record
Abstract
The main purpose of this study was to identify the range of supportive care needs of patients diagnosed with lung cancer who attended an outpatient, regional cancer centre. Lung cancer has more than a physical impact on those who are diagnosed with the disease, yet relatively little has been reported on their needs beyond those for physical symptom management. A total of 88 patients participated in this study by completing a self-report questionnaire. The data provided clear indication that a range of needs, both physical and psychosocial, exist for this group of patients and, furthermore, remain unmet. Lack of energy, pain, and concern about those close to them were reported most frequently. Patients also expressed distress because of difficulty managing their needs and many indicated wanting help to cope with the challenges they were experiencing. However, a sizeable proportion (45% to 58%) indicated they did not want help from staff at the cancer centre for some need items despite considerable distress arising from those remaining unmet (e.g., lack of energy, fears about cancer spreading, not being able to do the things you used to do). Suggestions for practice and future research are offered to improve the care for this group of patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| 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 | 0.000 |
| 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".