Impact of Patient Smoking Behavior on Empathic Helping by Family Caregivers in Lung Cancer
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To test the impact of patient smoking behavior on family caregiver judgments of responsibility, emotions, empathic responses, and helping behavior. DESIGN: Structural equation modeling. SETTING: Five oncology outpatient settings in Canada. SAMPLE: 304 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, empathic responses, and helping behaviors. FINDINGS: The impact of patient smoking behavior on caregiver help was mediated by caregiver judgments of responsibility, affective reactions of anger and pride, and empathic responses by caregivers. CONCLUSIONS: When patients continued to engage in smoking behavior, despite a diagnosis of lung cancer, caregivers tended to ascribe more responsibility and feel more anger and less pride in the patients' efforts to manage the disease, therefore placing caregivers at risk for less empathy and helping behavior. IMPLICATIONS FOR NURSING: Caregiver blame and anger must be assessed, particularly when the patient with lung cancer continues to smoke. If caregiver judgments of blame and anger are evident, then an attribution approach is indicated involving a dialogue between the caregiver and the patient, with the aim of enhancing the caregiver's understanding of how negative attributions and linked emotions impact his or her ability to engage in empathic helping behaviors.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".