Preceptors' Subjective Competency Ratings in Acute Care Hospitals in Taiwan
Bibliographic record
Abstract
BACKGROUND: This study focused on developing a Subjective Competency Scale (SCS) in acute care hospitals and identified factors that affect preceptors' competency to precept new graduate nurses (NGNs). METHOD: This study was conducted in two stages that included collecting information on preceptor training courses and conducting a cross-sectional questionnaire survey. A total of 350 preceptors completed the survey in 2011. The validity and reliability of the SCS were determined. RESULTS: An SCS was developed using 22 items and five factors: teaching/assessment skills, interpersonal/communication skills, confidence/self-assurance, problem-solving/stress-coping skills, and self-reflection. These explained 69.73% of the variance. Cronbach's alpha for these five factors of scale ranged from .715 to .889. Preceptors' subjective competency was correlated positively with age, years as a nurse, years as a preceptor, willingness to be a preceptor, and self-rated relationship with NGNs (p < .001). CONCLUSION: The SCS exhibited high validity and reliability; therefore, it can be used for future preceptors' subjective competency assessment and evaluation. [J Contin Educ Nurs. 2019;50(2):69-78.].
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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 | 0.002 | 0.009 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".