Geriatric nursing competence and training needs among dermatology nurses: A cross-sectional study
Bibliographic record
Abstract
Objective: To investigate the current status of geriatric nursing competence among dermatology nurses and analyze its potential related factors. Methods: A cross-sectional quantitative study was conducted with 550 dermatology nurses from 20 medical institutions in China. Data were collected using a questionnaire containing sociodemographic variables and the Geriatric Nursing Competency Assessment Scale for Clinical Nurses, which includes primary, secondary, and tertiary dimensions. Multivariate linear regression was used for analysis. Results: The overall mean score for geriatric nursing competence among dermatology nurses was 2.36 ± 0.90. Among the three primary dimensions, “professional competence” received the highest score (2.46 ± 0.89). Of the ten secondary dimensions, “critical thinking” achieved the highest score (2.48 ± 0.96), while “research and innovation” received the lowest score (2.15 ± 1.05). Experience in caring for elderly patients, the duration of geriatric nursing training, and the presence of patients aged ± 60 years were the main factors related to geriatric nursing competence. The level of the healthcare institution, educational background, work experience, and the degree of specialization in geriatric nursing training were statistically significant factors influencing dermatology nurses' competence. Conclusions: The geriatric nursing competence of dermatology nurses is moderate. There is an urgent need to strengthen research development and promote professional growth.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".