A comparison of patient and family caregiver prospective control over lung cancer
Bibliographic record
Abstract
AIM: This paper is a report of our secondary analysis of patient and family caregiver prospective control in lung cancer. BACKGROUND: Control beliefs underlie self-care in sickness and health. Self-care often involves 'shared' activities between the afflicted individual and caregiving family and friends. However, depending on how control is perceived, conflicts can occur in decision-making thus jeopardizing optimal self-care. We need to comprehend how control beliefs compare between patients and caregivers and how their control beliefs are linked with dealing with serious illness. METHODS: Based on questionnaire data collected in our larger study between September 2005 and February 2009, we conducted exploratory comparative analyses of 304 patients' and caregivers' control beliefs in managing lung cancer. Eight 5-point response items captured prospective control. Exploratory factor analysis with promax rotation was conducted to compare dyadic perceptions on the dimensionality of prospective control. We also conducted exploratory correlations between control beliefs and smoking cessation, attributional reactions, caregiver helping and symptom reports. RESULTS: Principal component analysis identified the same factors for patients and caregivers: factor 1, Fate control and factor 2, Team control. Patient and caregiver 'Fate' and 'Team' control sub-scales were respectively associated with hope, caregiver helping and patient smoking cessation. CONCLUSION: Clinicians need to support, adapt or develop a philosophy of cancer care that is inclusive of partnerships, drawing on beliefs of patients and caregivers that controlling lung cancer is a team effort which in turn is tentatively linked to patient smoking cessation, positive emotions and caregiver helping.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".