Cultural competencies of selected health careworkers in the West Pomeranian Voivodeship
Bibliographic record
Abstract
AMA Słowik K, Jersak E, Schneider-Matyka D, Cybulska A, Rachubińska K, Grochans E. Cultural competencies of selected health care workers in the West Pomeranian Voivodeship. Nursing Problems / Problemy Pielęgniarstwa. 2024;32(1):30-37. doi:10.5114/ppiel.2024.139131. APA Słowik, K., Jersak, E., Schneider-Matyka, D., Cybulska, A., Rachubińska, K., & Grochans, E. (2024). Cultural competencies of selected health care workers in the West Pomeranian Voivodeship. Nursing Problems / Problemy Pielęgniarstwa, 32(1), 30-37. https://doi.org/10.5114/ppiel.2024.139131 Chicago Słowik, Karolina, Elżbieta Jersak, Daria Schneider-Matyka, Anna Cybulska, Kamila Rachubińska, and Elżbieta Grochans. 2024. "Cultural competencies of selected health care workers in the West Pomeranian Voivodeship". Nursing Problems / Problemy Pielęgniarstwa 32 (1): 30-37. doi:10.5114/ppiel.2024.139131. Harvard Słowik, K., Jersak, E., Schneider-Matyka, D., Cybulska, A., Rachubińska, K., and Grochans, E. (2024). Cultural competencies of selected health care workers in the West Pomeranian Voivodeship. Nursing Problems / Problemy Pielęgniarstwa, 32(1), pp.30-37. https://doi.org/10.5114/ppiel.2024.139131 MLA Słowik, Karolina et al. "Cultural competencies of selected health care workers in the West Pomeranian Voivodeship." Nursing Problems / Problemy Pielęgniarstwa, vol. 32, no. 1, 2024, pp. 30-37. doi:10.5114/ppiel.2024.139131. Vancouver Słowik K, Jersak E, Schneider-Matyka D, Cybulska A, Rachubińska K, Grochans E. Cultural competencies of selected health care workers in the West Pomeranian Voivodeship. Nursing Problems / Problemy Pielęgniarstwa. 2024;32(1):30-37. doi:10.5114/ppiel.2024.139131.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".