A survey of skin cancer screening practices among dermatology nurses.
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to survey the current level of participation dermatology nurses have in screening and skin cancer detection. SAMPLE: Nursing professionals including registered nurses (RNs) and licensed practical nurses and their Ontario, Canadian equivalents (registered practical nurses), advanced practice nurses, such as nurse practitioners and dermatology nurse practitioners, were included in the sample. RN education ranged from associate's to master's degree preparation. DATA ANALYSIS: Demographic, clinical setting, and practice information were reported using descriptive statistics and cross tabulations. RESULTS: Eighty-three percent (n = 89) of nurses surveyed are performing a total-body skin examination (TBSE); 15% (n = 16) are confident and 52% (n = 56) are very confident with their skills. CONCLUSIONS: The vast majority of nurses surveyed reported that they practiced skin cancer screening. They also reported using dermatologic tools, such as dermatoscope and digital camera. Nurses trained to perform TBSEs, as well as those who use dermatoscopes and digital cameras when performing TSBEs, provide an important component in improving cancer screening and detection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".