Characteristics of caregiving: A prospective, observational study of lung cancer patients and their informal caregivers.
Bibliographic record
Abstract
18 Background: Cancer self-management involves active partnership between patients and their informal caregivers (ICs). There is a dearth of literature on ICs to lung cancer patients. Multi-modality treatment and profound challenges in symptom management and lifestyle adjustment are hallmarks of this population. This study aimed to describe the characteristics of, and resources utilized by ICs to lung cancer patients and examine the association between symptom severity and a) caregiver burden and b) perceived support. Methods: This study was conducted at a cancer centre north of Toronto, Canada. Dyads of lung cancer patients receiving outpatient treatment and their self-identified ICs (N = 39) were recruited. Upon consent, participants completed a one-time survey which assessed study variables employing previously validated instruments, including: patient’s functional status, caregiver burden, caregiver’s perceived social support and utilization of resources to enhance self-management. Descriptive analysis was used to describe our sample and frequency of resource utilization. Pearson’s correlation was used to examine the association between symptom severity and a) caregiver burden and b) perceived support. Results: The study sample consisted of middle-aged patients and caregivers (median 55-64 years). A majority of caregivers were female (76.2%), received education above college level (56.1%) and were immediate family members (80.9%). The most frequently utilized resources were the lung cancer patient handbook (48.8%), followed by personal support worker (29.3%). Caregiver support group was the least utilized (10%) resource. Patient’s symptom severity was negatively correlated with one aspect of caregiver burden, caregiver’s self-esteem (r = -0.36, p < 0.05). Conclusions: Findings indicated similarities in caregiver demographics to carers of other patient populations. Informational support and material aid appeared to be the most important resources. Patients’ well-being had the greatest impact on caregivers’ self-esteem, indicating implications on person-centred care and collaborative patient-provider relationships to support patient self-management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".