The importance of partners for the secondary prevention of Melanoma: A study examining the skin self-examination self-efficacy of patients and their partners
Bibliographic record
Abstract
The increasing incidence of melanoma, its poor prognosis if diagnosed at a late stage, and high risk of recurrence, makes early detection and prevention of this chronic and life-threatening disease of utmost importance. Skin self-examination (SSE) and partner-assisted skin-examination (PASE) have been recognized as effective methods for the early detection of melanoma in high-risk populations. Variables that have been associated with the performance of SSE in melanoma patients include: patient-reported self-efficacy, partner support, relationship satisfaction, comfort with SSE, and partner attendance at a SSE education session. In order to elucidate these relationships, three studies were undertaken. Study 1 involved the adaptation and validation of the Received Support Scale of the Berlin Social Support Scales (BSSS-RS) in a Canadian sample of patients with melanoma. This measure assesses illness support received from one's partner, and was utilized in the subsequent chapter to assess general illness support as a predictor of patient SSE self-efficacy. The BSSS-RS was found to have excellent reliability in French and English-Canadian participants (N = 137), and was found to correlate more with measures assessing proximal constructs, while correlating less with measures assessing distal constructs. Study 2 examined whether patient reports of relationship satisfaction, general illness support, SSE support, and SSE comfort, as well as partner attendance at a SSE education session, predicted SSE self-efficacy in patients with melanoma (N = 137). SSE support and SSE comfort were found to significantly predict SSE self-efficacy in patients with melanoma. Furthermore, the relationship between SSE comfort and SSE self-efficacy was moderated by partner attendance at a SSE education session. Study 3 examined whether partner (n = 100) and patient (n = 100) SSE self-efficacy, were predicted by partner reports of SSE comfort, SSE support, and partner attendance at a SSE education session. Findings highlight the importance of partner involvement in SSE education for increased SSE self-efficacy in both patients with melanoma, and their partners. Moreover, results underscore the importance of comfort with SSE and SSE support for patient and partner SSE self-efficacy. Implications for research and practice are discussed.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".