Antimicrobial resistance in Ecuador: A One Health situational analysis of governance, infrastructure, and equity gaps
Bibliographic record
Abstract
Antimicrobial resistance (AMR) represents a growing global public health threat, undermining therapeutic efficacy and disproportionately affecting vulnerable populations. In Ecuador, the situation is exacerbated by weak surveillance systems and fragmentation among the human, animal, and environmental health sectors. This study analyzes current national approaches that address AMR in Ecuador from a One Health (OH) perspective, with an emphasis on governance, public policy, health infrastructure, and equity. A qualitative approach was employed combining document review, scientific literature analysis, and semi-structured interviews with key informants (KIs) engaged across all OH sectors. Findings reveal structural barriers, including lack of institutionalization of the OH strategy, limited funding for the health system, weak intersectoral coordination, low community awareness, and the absence of equity-oriented policies. In conclusion, the results highlight the urgent need to strengthen the governance of antimicrobial stewardship in Ecuador through strategies that are contextually adapted, aligned with international recommendations, and grounded in a comprehensive multisectoral approach.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".