Evaluating the efficacy of Jahezon's educational program on critical nursing care: A quasi-experimental study
Bibliographic record
Abstract
Objective: Competent and trained nurses are essential for critical care preparedness. This study assesses the impact of an educational training program (Jahezon) on the participants' critical care knowledge by comparing their pre- and post-knowledge outcomes.Methods: This study used a quasi-experimental one-group pre-test-post-test design to examine the impact of 40 days of theoretical and practical training on 43 selected nurses from 16 hospitals located in the Hai’l Health cluster, situated in the city of Hai’l, Saudi Arabia. The training program started on November 2021, the curriculum covered a comprehensive range of critical care nursing concepts and was divided into four phases. The instrument used to assess knowledge was a 50-item multiple-choice questionnaire, which was administered as a pre-test, post-test, and a 6-month follow-up test format. The data were analyzed using SPSS v29.0.Results: The mean total scores were the highest in the follow up test (M = 9.87, SD = 2.34), followed by the post-test (M = 7.57, SD = 0.98) and the pre-test (M = 5.91, SD = 1.06), showing a statistically significant difference (F(2, 117) = 64.834, p < .001). From the pre-test to the post-test, 93% of the total scores improved. The only demographic factor that affected the test scores was gender, with female nurses scoring higher.Conclusions: The nurses' knowledge improved significantly after their participation in the critical care training program, but more research is needed to determine their actual performance in caring for critically ill patients during a pandemic.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".