Stress in Patients With Lung Cancer: A Human Response to Illness
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To provide a comprehensive overview of stress in patients diagnosed with lung cancer within the context of the four perspectives (normal physiologic, pathophysiologic, behavioral, and experiential) of the Human Response to Illness Model. DATA SOURCES: Published research articles, clinical articles, book chapters, and Internet sources on stress and lung cancer. Initial literature searches in CINAHL(R) and PubMed(R) focused on data subsequent to 2001; classic research dating back to the 1970s also was included. DATA SYNTHESIS: Patients diagnosed with lung cancer experience psychological and biologic stressors from a delayed cancer diagnosis, symptom management issues, and social stigmatization of their illness. These stressors may cause a physiologic stress response, exacerbate the disease process, and decrease the patient's quality of life. CONCLUSIONS: Acknowledging that the stress response may interact with pathophysiologic disease processes such as lung cancer is important, and stress management in patients with cancer should include all four perspectives of the Human Response to Illness Model. IMPLICATIONS FOR NURSING: By examining the four perspectives, interventions may be implemented to prevent or alleviate the detrimental effects of the pathophysiologic stress response. This article establishes the relevance of this nursing model to assess and manage stress among patients with lung cancer and other types of cancers.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".