New nurses’ transition to practice experience after completion of the enhancement training program in the Sultanate of Oman
Bibliographic record
Abstract
In 2021, the Directorate General of Nursing Affairs (DGNA) at the Ministry of Health (MOH) in the Sultanate of Oman has established a 12-month training program for new nursing graduates known as the Enhancement Training Program (ETP). The program aims to intensively train novice nurses in seven critical specialties and equip them with the specialty knowledge, skills, and professional competence required to deliver safe and specialized care to critically ill patients. This study aims to determine the levels of confidence, comfort, satisfaction, stress, and experiences of transition-to-practice among nurses who completed the program successfully, and to assess the differences in the levels of confidence, comfort, satisfaction, stress, and experiences of transition-to-practice among the nurses participated in different specialties. The study sample target the first cohort of nurses who participated in the ETP from June 2021 to June 2022 and passed the program successfully (n = 313). Casey-Fink Graduate Nurse Experience Survey was utilized to evaluate the experiences of the participating nurses. Statistical Package for the Social Sciences (SPSS) version 29 software used to analyze the collected data by performing descriptive analysis using means and standard deviations. Inferential analysis (analysis of variance [ANOVA]) was conducted to assess the variance between the groups so as to identify any statistical differences among the means of all the specialty groups. The findings of this study provide evidence supporting the effectiveness of the ETP in boosting the confidence, comfort, and satisfaction of novice nurses.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".