Factors Influencing Development of Professional Values Among Nursing Students and Instructors: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Professional values are standards of behavior for performance that provide a framework for appraising beliefs and attitudes that influence behavior. Development of professional values has been a continuous and long process and it is influenced by different factors. The aim of this study is "assessing different factors influencing development of professional values among nursing students and instructors". METHOD: In this systematic review, a broad research was performed to find articles from Persian and English databases: pub Med, Pro quest, Elsevier, SID, Google scholar, Ovid and Iran Doc; nursing student, instructors, ethics, professional value, ethical value and educators were used as the key words. Among 3205 achieved articles, by eliminating repeated ones, 22 articles were assessed during the period 1995-2013. Data achieved from the articles were summarized, categorized and analyzed based on the research question. RESULTS: In this study "education and achieving professional experiences", "Students and instructors' perspectives on professional values", "the role of culture in considering and developing professional special values" and "the effect of learners' individual characteristics" were extracted as the four main themes. CONCLUSION: Considering the effect of educational, cultural and individual factors in developing nurses' professional values; it is recommended to the educational and health centers to consider value-based cares in clinical environments for the patients in addition to considering the content of educational programs based on ethical values in the students' curriculum.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".