Employing Kirkpatrick’s framework to evaluate nurse training: an integrative review
Bibliographic record
Abstract
to evaluate the evidence on the use of Donald Kirkpatrick's framework in nursing training evaluation. integrative literature review in the Latin American and Caribbean Health Sciences Literature, Medical Literature Analysis and Retrieval System and Web of Science databases. Studies that answered the review question "Which is the evidence in using Donald Kirkpatrick's framework to evaluate training in the nursing workplace?" published in Portuguese, English, or Spanish were included. out of 108 studies retrieved, thirteen were included. The majority evaluated the four levels proposed in the model (reaction, learning, behavior, and results) or, at least, a combination of the first three ones. Different instruments were used to evaluate nursing training, mainly in quantitative approaches for reaction and learning levels and qualitative for behavior and results levels. This approach highlights the flexibility of the model and the importance of choosing a reliable set of instruments, which is crucial to qualify the analysis at each level. Kirkpatrick's model has been used worldwide to evaluate training in the nursing field and has been shown to be suitable for it, as long as there is an appropriate selection of instruments at each level. BACKGROUND: (1) Kirkpatrick's framework is effective for evaluating various nursing training. (2) The framework upholds the choice of measuring instruments for each level. (3) The four-levels or a combination of the first three were the most commonly used to evaluate training. (4) The four-levels or a combination of the first three were the most common to evaluate training. (5) Evaluation of results in organizational practices is the most challenging level.
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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 | 0.060 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.046 | 0.037 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".