Nurses’ Attitudes Toward Lifelong Learning via New Technologies
Bibliographic record
Abstract
Introduction: Lifelong professional education is considered as a qualitative indicator in the health discipline, as it can improve health professionals’ knowledge and skills, as well as nursing care. Purpose: The purpose of this original research is to examine and record the attitudes and behavior of nurses working in state-run hospitals in the Municipality of Thessaloniki regarding lifelong education through new technologies. Identification of nurses’ motivations for lifelong distance education, recording of nurses’ perception of the need for continuing nursing education, and determining how nurses pursue lifelong learning are the objectives of this study. Methodology: The study was conducted between January and March 2019. The sample of the study consisted of 124 nurses (n = 124) from three state hospitals of the Municipality of Thessaloniki. A questionnaire consisting of 5 parts was used as a research tool. SPSS 23 statistical software platform was used for statistical analysis. Results: The sample consisted of 124 participants, 12 were men and 112 were women. The mean age of the participants was 42.37 years and the mean experience in the field was 16.78 years. Two main reasons for continuing education were attributed to the upgrade of the nursing profession and the need to improve the quality of care provided. Conclusions: Nurses believe that continuing education is essential and their professional knowledge must periodically be enriched and renewed.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".