Feasibility Study of an Online Intervention to Support Male Spouses of Women With Breast Cancer
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To evaluate the feasibility of a web-based psychosocial supportive intervention entitled Male Transition Toolkit (MaTT). . DESIGN: Randomized, controlled trial, mixed methods, concurrent feasibility design. . SETTING: Edmonton, a large metropolitan city in western Canada. . SAMPLE: 40 dyads (women with breast cancer and their spouse). . METHODS: Male spouse participants in the treatment group accessed MaTT for four weeks. Data on hope, quality of life, general self-efficacy, and caregiver guilt were collected at baseline and days 14, 28, and 56. Quality-of-life data were collected from the women with breast cancer at each time period. Qualitative data were collected from the usual care group in an open-ended interview and from the treatment group in an evaluation survey on days 14 and 28. . MAIN RESEARCH VARIABLES: Feasibility, as measured by the MaTT questionnaire. . FINDINGS: Evaluation survey scores indicated that MaTT was feasible, acceptable, and easy to use. Male spouse quality-of-life scores were not significantly different between groups. As guilt scores decreased, male spouses' quality of life increased. . CONCLUSIONS: The findings provided useful information to strengthen MaTT and improve study design. Additional research is needed to determine its efficacy in improving male spouses' quality of life. . IMPLICATIONS FOR NURSING: MaTT is a feasible intervention. Future research should evaluate MaTT with larger samples as well as determine the amount of time participants used MaTT.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".