Sense of self-efficacy of nursing staffand their willingness to write prescriptionsand prescribe medicines
Bibliographic record
Abstract
AMA Sikorska M, Strzelecka A, Kozieł D. Sense of self-efficacy of nursing staff and their willingness to write prescriptions and prescribe medicines. Nursing Problems / Problemy Pielęgniarstwa. 2023;31(4):196-200. doi:10.5114/ppiel.2023.136176. APA Sikorska, M., Strzelecka, A., & Kozieł, D. (2023). Sense of self-efficacy of nursing staff and their willingness to write prescriptions and prescribe medicines. Nursing Problems / Problemy Pielęgniarstwa, 31(4), 196-200. https://doi.org/10.5114/ppiel.2023.136176 Chicago Sikorska, Magdalena, Agnieszka Strzelecka, and Dorota Kozieł. 2023. "Sense of self-efficacy of nursing staff and their willingness to write prescriptions and prescribe medicines". Nursing Problems / Problemy Pielęgniarstwa 31 (4): 196-200. doi:10.5114/ppiel.2023.136176. Harvard Sikorska, M., Strzelecka, A., and Kozieł, D. (2023). Sense of self-efficacy of nursing staff and their willingness to write prescriptions and prescribe medicines. Nursing Problems / Problemy Pielęgniarstwa, 31(4), pp.196-200. https://doi.org/10.5114/ppiel.2023.136176 MLA Sikorska, Magdalena et al. "Sense of self-efficacy of nursing staff and their willingness to write prescriptions and prescribe medicines." Nursing Problems / Problemy Pielęgniarstwa, vol. 31, no. 4, 2023, pp. 196-200. doi:10.5114/ppiel.2023.136176. Vancouver Sikorska M, Strzelecka A, Kozieł D. Sense of self-efficacy of nursing staff and their willingness to write prescriptions and prescribe medicines. Nursing Problems / Problemy Pielęgniarstwa. 2023;31(4):196-200. doi:10.5114/ppiel.2023.136176.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".