CAREGIVING FOR A RELATIVE WITH LUNG CANCER: HOW DO SENIORS COMPARE TO YOUNGER CAREGIVERS?
Bibliographic record
Abstract
Lung cancer is the most frequent type of cancer diagnosed. With a 5-year survival of 17%, such diagnosis is emotionally threatening not only for patients but also for their family caregivers (FC). Studies have reported even higher distress for FC. However, few researchers have focused on this population, particularly in longitudinal surveys. This study compares, for different age groups, distress and quality of life (QoL) among FC of lung cancer patients. Participants completed every 3 months, up to 9 months, validated questionnaires on distress and QoL. Univariate, bivariate analyses and mixed models with repeated measurements were conducted. A total of 105 FC participated to the survey (N=43 aged < 60, N=32 aged 60–69 and N=30 aged ≥ 70). In all groups, FC were predominantly women. Older FC had more frequently health problems. However, at baseline, distress was significantly lower among seniors (IDPESQ score 33.7 in < 60; 27.7 in 60–69; 22.8 in ≥ 70 (score range 0–100); p=0.04). Also, they experienced significantly better global QoL (p=0.008), physical (p=0.01) and psychological (p=0.003) well-being, as well as less social preoccupations (p=0.008) than their younger counterparts. These differences remained after 3, 6 and 9 months. These results suggest that seniors better manage caregiving than younger FC. However, with the expected increase in the prevalence of cancer, the aging of the population and the growing number of frail seniors, more research is necessary in this field, to better document FC experience in order to adapt services to their needs, especially for the very old.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".