Antimicrobial resistance in long-term care facilities
Bibliographic record
Abstract
Long-term care facilities (LTCFs) provide care for extended periods to older people who frequently require antimicrobials to treat and prevent infection, a leading cause of morbidity and mortality among older LTCF residents. Evidence indicates that, due to a combination of factors related to LTCF residents, prescribers and health care systems, up to 75% of antimicrobial prescriptions in LTCFs are inappropriate, in terms not only of the duration and choice of therapy, but also the need for therapy in the first place. Inappropriate use of antimicrobials is associated with the high rates of multi-drug resistant organisms that are recovered in LTCFs, and may exacerbate the threat of antimicrobial resistance (AMR), both in LTCFs and in the community. Yet, policies to tackle inappropriate antimicrobial use and AMR in LTCFs, such as antimicrobial stewardship and infection prevention and control (IPC), remain underused or suboptimal. Some countries are starting to act but they are a minority. Countries seeking to improve antimicrobial consumption, and minimise the threat of AMR, in LTCFs can: set up routine surveillance systems dedicated to collecting and reporting data on antimicrobial use and resistance in LTCFs; design, implement and enforce multifaceted antimicrobial stewardship programmes that comprehensively address multiple determinants of inappropriate antimicrobial prescribing and use; and adopt IPC programmes tailored to the specific needs and risks of LTCFs.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".