We think we can: development of the Dyadic Efficacy Scale for Cancer
Bibliographic record
Abstract
Abstract Background: Measurement advances are needed to enable the study of dyadic-level processes impacting couples coping with cancer. This study sought to develop and empirically examine a Dyadic Efficacy Scale for Cancer (DESC). Cancer-related dyadic efficacy is an individual's confidence to work together with a partner to cope with cancer and its treatment. Methods: The DESC was developed using an exploratory sequential mixed methods design. This paper outlines the psychometric evaluation phase. Individuals with cancer (N = 261) and their partners (N = 217) completed 50 items. Item-level analyses reduced this set to 26 items. Using the dyad as the unit of analysis, confirmatory factor analysis with mirrored patient and partner bifactor structure tested for the presence of a general factor and 3 secondary factors, that is, illness intrusions, patient affect, partner affect. Results: Goodness-of-fit indices supported the identified model, χ 2(1170) = 2090, P < .001; RMSEA = .05, P = .14, 90% CI .05–.06; SRMR = .05; CFI = .90. Multidimensionality differed for patients and partners. A general dyadic efficacy factor and secondary factors for managing affect were present for both dyad members, whereas the secondary factor of managing illness intrusions was confirmed for patients only. The model explained 72% and 64% of the variance in patients’ and partners’ dyadic efficacy. Evidence of convergent validity was presented. Conclusions: This study is the first to provide a tool to assess dyadic efficacy among couples coping with cancer. The assessment of cancer-related dyadic efficacy enables new discoveries into couples’ adjustment to 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".