Effectiveness of learning intramuscular injection techniques with aid of an interactive APP
Bibliographic record
Abstract
In this study, an interactive application (app) for learning intramuscular injection is developed through an interdisciplinary collaboration, and its effectiveness in learning is explored. APP is application software. The research object is a second-year student of the nursing department of a university of science and technology. The study results indicated that only 1 out of 57 students failed to obtain a score of 60 on a technical test, whereas the other students all scored 60 or higher (a score of 60 or higher is regarded as the acceptable range). For the self-assessment of learning effectiveness, the three items with the highest average scores were “It helps students to improve their self-confidence,” “It helps the integration with practical operations,” and “It enhances my after-school learning and meets my learning needs.” Furthermore, the students indicated that the interactive app helped them clarify technical procedures and precautions, deepen their knowledge, and clarify clinical concepts. Moreover, more planning and communication is required for the teaching of nursing techniques and cross-disciplinary integration of technologies. This study aims to provide a reference for nursing educators and integrate technology applications in nursing teaching to enhance the learning effectiveness of students in relation to nursing techniques.
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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".