Furthering systemic inquiries into couples coping with cancer through adaptation and extension of a dyadic efficacy model
Bibliographic record
Abstract
Cancer is a dyadic stressor that exerts substantial effects on patients' and their partners' psychological, social, physical and spiritual well-being.Patients' and partners' responses to these effects do not occur in isolation but take place within the dynamic, interdependent system of the dyad.Dyadic perspectives on individually focused models of coping with cancer have successfully been accomplished, but similar expansions have not yet extended to the study of self-efficacy.Self-efficacy expectations reflect an individual's perception of his or her capability to accomplish a task.Dyadic efficacy shifts the focus of capability from the individual to the couple.Cancer-related dyadic efficacy is an individual's judgement of his or her confidence to conjointly manage the effects of cancer and its treatment together with a partner.Building on existing self-efficacy scholarship in psychosocial oncology and a dyadic efficacy model used in rheumatoid arthritis research, this dissertation research represents the first empirical efforts to examine cancer-related dyadic efficacy.A multi-phase exploratory sequential mixed-methods design was used to (a) conceptualize dyadic efficacy in the cancer context and (b) develop and evaluate a Dyadic Efficacy Scale for Cancer (DESC).The exploratory phase involved the use of focus groups for data collection and was informed by a collective qualitative case study design.Quantitative data collection followed a single-time point survey design for both pilot and psychometric testing.A secondary analysis of qualitative data was then conducted to facilitate the (c) identification of facilitators and obstacles to cancer-related dyadic efficacy.Three distinct samples of patients and partners were recruited, resulting in the total participation of 296 patients and 240 partners.Eligible patients were currently undergoing or recently completed (within 6 months) treatment for cancer and were involved in a committed relationship of at least one year.Thematic analysis was used to describe cancer-related dyadic efficacy, identify assessment domains and categorize Psychology Research Group, is also listed as co-author on manuscripts 1 and 3 due to her contribution to the qualitative data analysis.
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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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".