Sense of self-efficacy of nursing staffand their willingness to write prescriptionsand prescribe medicines
Bibliographic record
Abstract
Introduction: A sense of self-efficacy plays a significant role in the work of nurses.It influences the motivation of this professional group to take on new tasks and expand their competences.This translates into patient satisfaction and impacts the functioning of the entire healthcare system.The aim of this study was to determine how the sense of self-efficacy of nursing staff affects their willingness to write prescriptions and prescribe medicines. Material and methods:The study was carried out between May and September 2021 in 19 primary healthcare facilities located in Kielce.Two facilities from each of the 13 districts of Świętokrzyskie Province were also drawn to participate in the study.The study was conducted among 188 nurses.The directors of the drawn establishments gave their written consent for the survey to be carried out at the respective primary health care facility.The list of all facilities in the Świętokrzyskie region was compiled based on data from the National Health Fund on entities that provide primary health care services.The research tools were an original survey questionnaire and the Generalised Self-Efficacy Scale (GSES).Results: Nurses with competences to issue prescriptions were characterised by higher scores of generalised selfefficacy.Based on the estimated logistic regression it can be concluded that the chance of willingness to prescribe medication is 10 times higher in nurses who are competent in relation to those who are not (OR = 9.934, 95% CI: 3.067-32.172,p < 0.001) and who have a higher sense of self-efficacy (OR = 3.559, 95% CI: 1.463-8.653,p = 0.005). Conclusions:The inclusion of the new competencies in nurses' career paths can help improve their sense of selfefficacy and contribute to their motivation to use their new powers.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".