Accepting a Terminal Cancer Prognosis: Associations with Patient and Caregiver Quality-of-Life Outcomes and Treatment Preferences
Bibliographic record
Abstract
Patients who are aware of their terminal cancer prognosis are more likely to receive end-of-life care consistent with their values. However, prognostic awareness has shown mixed associations with quality of life (QoL) outcomes. Based on theories of acceptance (i.e., Erikson’s stages of life development, Kubler-Ross’s stage model of grief, coping theories) and the Ottawa Decision Support Framework, acceptance of cancer may moderate relationships between prognostic awareness and QoL outcomes and end-of-life treatment preferences. Dyadic coping theories, such as the Systemic Transactional Model and the Dyadic Cancer Outcomes Framework, suggest that patients’ degree of prognostic awareness and acceptance of their illness may also impact their family caregivers’ QoL and end-of-life treatment preferences for the patient. The aim of the present study was to examine the potential moderating role of patient acceptance of cancer in the relationships between patient prognostic awareness and both patient and caregiver QoL and end-of-life treatment preferences. This study was a secondary analysis of cross-sectional data from advanced cancer patients (n = 243) and their caregivers (n = 87) enrolled in the multi-institutional Coping with Cancer-II study. Patient outcomes of physical, psychological, and existential QoL were examined in a moderation path analysis. Caregiver physical and psychological QoL were examined in separate moderation regressions. Patient and caregiver end-of-life treatment preferences were examined in multiple logistic regression moderation models. Results did not support my hypothesis, as patient illness acceptance did not moderate the relationships between patient prognostic awareness and patient and caregiver QoL outcomes and end-of-life treatment preferences. However, there were significant main effects of patient illness acceptance on their own physical, psychological, and existential QoL as well as caregiver psychological QoL. There were also significant main effects of patient prognostic awareness on their own physical QoL and both their own and their caregivers’ end-of-life treatment preferences. Findings suggest that increasing patient’s prognostic awareness and illness acceptance may help improve values-consistent end-of-life care and QoL outcomes in advanced cancer patient-caregiver dyads. Findings support timely conversations to promote advanced cancer patients’ prognostic awareness as well as further research examining the impact of acceptance-based interventions in advanced cancer.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".