Characteristics of nursing professionals and the practice of ecologically sustainable actions in the medication processes
Bibliographic record
Abstract
Objectives: to verify the correlation between the characteristics of professionals and the practice of sustainable actions in the medication processes in an ICU, and to determine if interventions such as training and awareness can promote sustainable practices performed by nursing staff in the hospital. Methods: before-and-after design study using Lean Six Sigma methodology, applied in an intensive care unit. Nursing staff were observed regarding the practice of ecologically sustainable actions during medication processes (n = 324 cases for each group (pre and post-intervention)) through a data collection instrument. The processes analyzed involved 99 professionals in the pre-intervention phase and 97 in the post-intervention phase. Data were analyzed quantitatively and the association of variables was accomplished by means of statistical inference, according to the nature of the related variables. Results: the education level was the only characteristic that showed to be relevant to an increase in sustainable practices, with a statistically significant difference (p = 0.002). When comparing before and after the intervention, there was an increase in environmentally friendly actions with statistically significant differences (p = 0.001). Conclusions: the results suggest that institutions should encourage and invest in formal education, as well as training of health professionals to promote sustainable practices in the hospital.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".