Development of a Training Content Index System for Pressure Injury Prevention Training Programs for Healthcare Assistants in Tertiary Hospitals
Bibliographic record
Abstract
Yan-Ying Zhu,1,* Cai-Xiang Zhang,1,* Lin Wang,1 Jin-Ai He,2 Jia-Jie Yan,3 Jing Liu4 1Extended Nursing, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, People’s Republic of China; 2The First Affiliated Hospital of Jinan University, Guangzhou, 510630, People’s Republic of China; 3Center for Bone, Joint and Sports Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, People’s Republic of China; 4School of Economics and Management, Changjiang Institute of Technology, Wuhan, 430212, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yan-Ying Zhu, Extended Nursing, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, People’s Republic of China, Tel +86-020-38688204, Email zhuyanying7396@126.comObjective: This study aims to develop an index system for a pressure injury prevention training program specifically designed for healthcare assistants in tertiary hospitals, providing a theoretical basis for training initiatives. The “index system” developed in this study refers to a structured framework outlining the content and components of training programs, rather than a performance evaluation tool.Methods: Based on a literature review and expert interviews, a customized expert consultation questionnaire titled “Pressure Injury Prevention Training System for healthcare assistants in Tertiary Hospitals” was created. The Delphi method was employed to conduct two rounds of consultations with 23 experts who met the selection criteria, resulting in the establishment of the final training program indicator system.Results: The average positive coefficient from the two rounds of expert consultations was 100%, with an authority coefficient of 0.823, and a coefficient of variation of 0.2037. The degree of consensus among expert opinions was 0.380 (P = 0.000). The finalized training system comprises seven primary indexes and 40 secondary indexes. Key training elements include repositioning techniques, the use of pressure-relieving devices, and skin cleansing methods, which were rated with high consensus by experts.Conclusion: The findings of this study demonstrate good representativeness and authority, serving as a valuable reference for developing pressure injury prevention training programs for healthcare assistants in tertiary hospitals. This index system serves as a structured framework outlining key knowledge, skills, and training delivery components, rather than a performance evaluation tool.Keywords: Delphi method, healthcare assistants, pressure injuries, training
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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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".