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Record W2964136490 · doi:10.1188/19.cjon.397-404

Skin Self-Examination: Partner Comfort and Support During Examinations as Predictors of Self-Efficacy in Patients At Risk for Melanoma Recurrence

2019· article· en· W2964136490 on OpenAlexaff
Julia DiMillo, Nathan C. Hall, Manish Khanna, Christine Maheu, Annett Körner

Bibliographic record

VenueClinical journal of oncology nursing · 2019
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsJewish General HospitalMcGill University
Fundersnot available
KeywordsMedicineMelanomaIncidence (geometry)DermatologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Skin self-examination (SSE) is an effective method for melanoma survivors to detect potential cancerous growths sooner. OBJECTIVES: The purpose of this study was to examine whether the SSE self-efficacy of patients with melanoma and their partners is affected by their partners' comfort and support during skin examinations. METHODS: 100 patient-partner dyads completed a 25-item sociodemographic questionnaire. Fifty-two partners attended an education session with the patient on skin examinations and the early detection of melanoma. All patients attended the education session. FINDINGS: Having their partners attend the education session, as well as being supportive and comfortable with skin examinations, significantly predicted patients' self-efficacy with SSEs. In addition, male patients were found to be significantly more comfortable with partner-assisted skin examinations and reported feeling more supported by their partner than female patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.024
GPT teacher head0.358
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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