Maintaining the Will to Live of Patients With Advanced Cancer
Bibliographic record
Abstract
The will to live is a natural instinct experienced by all human beings. It tends to persist in humans, despite marked adversity such as that associated with advanced cancer. The will to live may be measured directly, or indirectly, by assessing the desire for hastened death. Factors that may affect it include age, life stage, and physical and psychological distress. In particular, states of depression and hopelessness may precede the loss of the will to live. Other psychosocial variables that may affect the will to live include physical suffering, attachment security, self-esteem, and spiritual well-being. A number of screening tools are available to identify risk factors for the loss of the will to live. Awareness of these factors can guide interventions to preserve morale and maintain hope in patients faced with a terminal illness. Critical among these are the alleviation of physical and psychosocial distress and the establishment of a therapeutic alliance that is sensitive to the specific support needs of individual patients. Comfort and facility with such supportive interventions in oncology will require greater attention to the development of communication and relationship skills at both undergraduate and postgraduate levels of training.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".