Does Blaming the Patient With Lung Cancer Affect the Helping Behavior of Primary Caregivers?
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To examine whether primary caregivers' helping behaviors are predicted by their illness attribution reactions as proposed in Weiner's model. DESIGN: Latent-variable structural equation modeling. SETTING: Five oncology outpatient settings in central Canada. SAMPLE: 100 dyads consisting of patients with lung cancer and their primary caregivers. METHODS: Self-report questionnaires, abstracted medical record data, confirmatory factor analysis, and structural equation modeling. MAIN RESEARCH VARIABLES: Smoking history, judgments of responsibility for controlling the disease, anger, pride, and helping behaviors. FINDINGS: An interrelation was seen between judgments of responsibility toward patients to control aspects of the disease, affective reactions of anger and pride, and helping behavior. Anger and pride had a stronger influence on helping behavior than smoking history did. CONCLUSIONS: Judgments of responsibility for controlling lung cancer and anger toward patients put caregivers at risk for dysfunctional helping behavior, particularly if patients had a history of tobacco use. IMPLICATIONS FOR NURSING: Primary caregivers' affective states directly affect their helping behavior toward patients with lung cancer. Clinicians should be aware that caregivers who perceive the patient to be largely responsible for managing the disease also may be angry toward that patient. Angry caregivers are at risk of providing suboptimal helping behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".