Self-efficacy and comfort with partner-assisted skin examination in patients receiving follow-up care for melanoma
Bibliographic record
Abstract
The objective of this study was to examine the role of interpersonal variables on melanoma survivors' self-efficacy for performing skin self-examinations (SSEs) during melanoma follow-up care. Specifically, the impact of comfort with partner assistance for SSE, SSE support received from one's partner, general partner support, relationship satisfaction, as well as partner attendance at a SSE education session, were examined. One hundred and thirty-seven patients with melanoma between the ages of 18 and 70 years, who also reported being involved in a romantic relationship, received a standardized education on SSE, and completed self-report questionnaires. Results indicate that SSE support and SSE comfort predicted patients' SSE self-efficacy. Partner attendance at the SSE education moderated the relationship between SSE comfort and SSE self-efficacy. In other words, SSE self-efficacy was found to be affected by partner attendance at the SSE education only in cases where the patient reported lower levels of comfort having his or her partner assist with SSE. Results highlight the importance of partner involvement in SSE education, as well as patient comfort with a partner's assistance during skin examinations. Findings inform potential modifications to the follow-up care provided to melanoma survivors by demonstrating the importance of partner involvement in SSE education.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".