Helping Her Heal‐Group: a pilot study to evaluate a group delivered educational intervention for male spouses of women with breast cancer
Bibliographic record
Abstract
BACKGROUND: Distress in husbands of women with early-stage breast cancer may be equivalent to or even higher than their wives. Husbands often struggle to help and support their wives cope with the illness and its treatment. In response, we developed a five-session group educational counselling intervention (Helping Her Heal-Group (HHH-G)) for husbands of women with early-stage breast cancer. The primary aim of the current pilot study was to determine the acceptability and feasibility of HHH-G and to obtain a preliminary estimate of its impact on participating men's skills, self-confidence and self care. Secondary aims were to assess the impact of the intervention on both the participating spouses' and wives' ratings of marital quality and depressed mood. METHODS: The study employed a one-arm, pre-post-intervention design whereby participating men (n=54) and their wives (n=54) independently completed measures at baseline (T0), immediately following the last session (T1) and 3 months after the last session (T2). RESULTS: Overall, there was very high study retention (87%). On the basis of the questionnaire data, we found significant improvements in spouses' self-efficacy (p<0.001) and self-reported skills including wife support (p=0.003) and self-care (p<0.001). In addition, there was a significant improvement in wives' mood scores (p=0.003). Post-intervention interviews support acceptability and impact of the HHH-G intervention, and provide support for the group format of the program. CONCLUSIONS: The feasibility and acceptability of HHH-G was supported, and treatment outcomes suggest the potential benefits of the intervention. Phase III evaluation of HHH-G program is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".