A dyadic examination of patients' and caregivers' attachment orientations and mutually supportive care in cancer caregiving
Bibliographic record
Abstract
OBJECTIVE: Families play a pivotal role in supporting one another during cancer. Research suggests that supportive care interactions between patients and their caregivers can have a positive effect on the physical health and well-being of both members of the dyad. However, few studies have investigated how patient and caregiver personality characteristics intersect with their perceptions of supportive exchanges. Adopting an attachment theory perspective, our aim was to examine the dyadic effects of patient and caregiver attachment orientations on mutually supportive care. METHODS: Patients (n = 103) receiving cancer care and their caregivers (n = 99) completed a survey that comprised measures of attachment orientations (Experiences in Close Relationships Modified scale), and mutually supportive care (Shared Care Inventory, SCI-3): communication, decision-making and reciprocity. RESULTS: Actor-Partner Interdependence Models (APIMs) were used to examine the association between participants' attachment orientations on their own (actor effects) SCI-3 outcomes and those of the other person within the dyad (partner effects). Across the APIMs, the tendency was for an inverse relationship between attachment (anxious and avoidant orientations) and mutually supportive care. Inspection of the effects and dyadic patterns supported actor and couple models. CONCLUSIONS: Using a dyadic approach, it was possible to study both intrapersonal and interpersonal effects. Our findings point to interdependence within the cancer caregiving relationship and underscore the importance of considering how individual and relational ways of responding influence support. Attachment theory provides a framework for explaining the observed relationships and a basis for therapeutic intervention.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".