Is It Possible to Notice the Unmet Non-Medical Needs among Cancer Patients? Application of the Needs Evaluation Questionnaire in Men with Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Lung cancer is the most common cause of cancer death worldwide. It is the most frequently diagnosed cancer in men. Lung cancer causes not only physical symptoms related to the disease itself and its treatment but also numerous mental, social and spiritual problems. The aim of the study was to assess non-medical needs among male lung cancer patients during oncological treatment. MATERIALS AND METHODS: The study was conducted on a group of 160 men (mean age 67 years) treated for lung cancer from June 2022 until November 2022 in 5 oncological centers in Poland. The Needs Evaluation Questionnaire (NEQ) was used. The NEQ explores five areas of patients' needs: informative, connected with assistance/care, relational, material and psycho-emotional support. RESULTS: All participants (except one) expressed some unmet non-medical needs (mean and median 11). Male lung cancer patients indicated informative needs most frequently. There were no significant differences between expressed unmet needs based on age, place of residence, professional activity or marital status. CONCLUSIONS: The NEQ seems to be a proper instrument to explore the non-medical needs of cancer patients. Adequate measures to address the unmet needs of lung cancer patients could contribute to an improved quality of life.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".