Influencing Factors of New Nurses’ Competency Following Participation in a Preceptorship Program: Cross-Sectional Study
Bibliographic record
Abstract
Background: Preceptorship programs have been implemented in several hospitals across Indonesia to support new nurses during their transition period in the workplace. Many factors influence new nurses successfully transitioning into this new role. However, few studies have examined the factors that affect new nurses' competency. Objective: This study aimed to identify the factors influencing the competency of new nurses in a preceptorship program. Methods: This study used a quantitative approach with a cross-sectional design. Participants were 169 nurses who had been employed for less than 1 year in 2 hospitals. Participants were nurses undergoing an orientation period who were part of a preceptorship program. The study used instruments developed by the researchers and their team, which were tested for validity and reliability. The variables were self-efficacy, new nurses' adaptation, preceptor commitment, preceptor competency, and mentoring method. Data were analyzed using descriptive statistics, the χ2 test, and multiple logistic regression. Results: The median age of the 169 participants was 24 years, with the ages ranging from 22 to 30 years. Most of the participants were female (n=136, 80.5%), held a bachelor's degree (n=164, 97%), and had worked at Hospital X for 0 to 6 months (n=128, 75.7%). In terms of training experience, most participants had completed Basic Cardiac Life Support training (n=142, 84%). The independent variables that influenced new nurses' competency were gender (P=.02), training (P=.05), mentoring method (P=.001), preceptor commitment (P=.03), and preceptor competency (P=.001). A multiple logistic regression test further indicated that the mentoring method (P=.001; α=.05; OR .198), preceptor commitment (P=.03; α=.05; OR .296), and preceptor competency (P=.001; α=.05; OR .202) were influential variables for new nurses' competency. Conclusions: The mentoring method, preceptor commitment, and preceptor competency were identified as the factors that most strongly influence new nurses' competency. These results can be used to develop more effective preceptor programs. An effective preceptorship program requires preceptors who demonstrate both professional competence and personal characteristics. Preceptors have to possess adequate knowledge and skills to support the development of new nurses' competency.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".