Asia-Pacific initiative for rheumatology nurse education: An impact survey in China
Bibliographic record
Abstract
Background/Objective: Asia-Pacific Initiative for Rheumatology nurse Education (ASPIRE) is a faculty-led initiative established to meet the educational needs of rheumatology nurses in Asia Pacific in recognition of the expanding role of nurses in daily rheumatology clinical practice. The objective of this study is to measure the impact of ASPIRE workshop training on nurses’ levels of knowledge, confidence, attitudes and beliefs using a Before-after-control-impact (BACI) survey.Methods: A total of 210 nurses who completed both pre- and post-surveys were included in the BACI analysis. The intervention group (n = 111) refers to nurses who attended the ASPIRE workshop training held during the China Chronic Disease Management Forum in Baotou, Inner Mongolia in September 2019 whereas the control group (n = 99) refers to Chinese nurses that have never attended the ASPIRE training. Results: Overall level of knowledge significantly increased by 30% (5.63 pre- vs. 8.34 post-survey; p < .001), and overall level of confidence significantly increased by 29% among nurses who attended ASPIRE training (5.83 pre- vs. 8.39 post-survey; p < .001). Nurses in the control group demonstrated no significant increase in knowledge (6.18 pre- vs. 6.50 post-survey; p = .097) or confidence (6.46 pre- vs. 6.71 post-survey; p = .169) over the same period.}Conclusions: Nurses who attended the ASPIRE training workshop reported a significant increase in their levels of knowledge and confidence compared with a control group of nurses who have never undergone ASPIRE training. Training rheumatology nurses to acquire more in-depth knowledge and skills can help optimize their role in clinical practice to meet the greater demands of disease monitoring and long-term management of rheumatology patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".