Healthcare Managers’ Perception About the Sustainable Development Goals (SDGs)
Bibliographic record
Abstract
In 2015, about 190 United Nations Member States proposed an ambitious agenda, to be worked on by different actors in society, which was entitled 2030 Agenda. The document was divided into 17 Sustainable Development Goals (SDGs), which are broken down into 169 targets aiming to eradicate poverty and promote a decent life for all. This study aimed to evaluate the perception of healthcare managers about SDGs, especially SDG 3, which addresses Good Health and Well-Being. This cross-sectional observational study identified and analyzed the participants’ profiles through online forms with questions about the general perception of the SDGs, and questions related to SDG 3. The quantitative analysis of the results was performed, in percentage terms, and the qualitative analysis was performed using the five-point Likert scale. Twenty-one technical directors of healthcare services participated in the survey. According to the results, 14 (66.6) of the participants presented medium to high knowledge regarding SDG. In addition, 18 (85.7%) of these professionals understand that the SDGs are of high/very high importance to guide public policies. In general, there is a low expectation for the achievement of the 17 SDGs in Brazil, but it was highlighted that it should be a priority, which SDG could contribute to the achievement of SDG 3: Good Health and well-being as well as the vision of policy recommendations to achieve the SDG 3 targets. This analysis allows contact with SDG and enables a deeper discussion on the topic in healthcare services.
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 | 0.009 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".