„Motivation and problems in nursing continuing education“ (study in Pleven region)
Bibliographic record
Abstract
The current research was aimed to study the motivation and problems of nurses in hospitals and public health institutions concerning the continuing education. From November 2015 to April 2019 a descriptive study was conducted among 723 nurses in different settings (university and private clinics, nurseries, kindergartens, local hospitals) and 174 nursing graduates. By anonymous self-administered questionnaires information about the motivation, expectations, results and the problems related to continuing education was collected and processed by IBM SPSS v. 24. Nurses in all settings highly evaluate the continuing education and are motivated to participate. Most of them ranked a better payment as the first motivator, followed by career development. The predominant form they have been involved is on-site continuing education (over 70%) and only a quarter have had an opportunity to participate in other forms. The most serious obstacles for out-site forms were “lack of personal financial resources” and “lack of financial stimuli” after improved qualification. Most of the graduates prefer high-technology clinics with only 13.2% for outpatient care and community centres. The motives of graduates to participate in different forms of continuing education, as well as the problems they expect to face highly correlate to those of practicing nurses. The results of the study underlined that the nurses in Pleven region and graduates in nursing highly appreciate the role of continuing educat
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".