Evaluating the Effectiveness of Existing In-service Training Courses on Infection Prevention and Control in Nurses: An Evaluation Using the Kirkpatrick Model
Bibliographic record
Abstract
Background: Evaluating the effectiveness of educational courses is essential for ensuring high-quality healthcare. Objectives: This study assessed the effectiveness of current in-service training courses on infection prevention and control for nurses, using the Kirkpatrick evaluation model. Methods: This evaluative study was conducted at Shiraz Army Hospital in 2024, involving 40 nurses and 10 supervisors. The educational program consisted of interactive workshops held over two days, covering topics such as standard precautions, isolation procedures, and hand hygiene. The evaluation followed Kirkpatrick’s four levels: Reaction, learning, behavior, and impact. Nurses' reactions and knowledge were measured using validated questionnaires, while supervisors assessed behavioral changes. Data were analyzed with SPSS 26, using descriptive statistics and a one-sample t-test. Results: All four levels of the Kirkpatrick model were evaluated. Nurses reported a mean reaction score of 3.73 (SD = 0.80), reflecting positive feedback on the training. Knowledge scores significantly increased from a pre-test mean of 2.39 (SD = 0.74) to a post-test mean of 3.72 (SD = 0.74) (P < 0.001). Supervisors observed a behavioral improvement, with scores increasing from 2.34 (SD = 0.94) to 3.72 (SD = 0.74) (P = 0.004). Furthermore, the nosocomial infection index decreased from 0.7 to 0.5 (P = 0.002) following the training. Conclusions: The findings demonstrate the effectiveness of current in-service training courses on infection prevention and control for nurses. The Kirkpatrick model proved to be a valuable evaluation tool, underscoring the importance of ongoing assessment of nurses’ competencies to enhance infection prevention practices.
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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".