Infection prevention and control in healthcare facilities in Albania
Bibliographic record
Abstract
Aim: The objective of this study was to assess the current status regarding Infection Prevention and Control (IPC) in selected healthcare facilities in Albania in light of the ongoing COVID-19 pandemic which continues unabated. Methods: A cross-sectional study was conducted in April 2021 including a nationwide representative sample of 505 health professionals working mostly in primary health care centres in Albania (84 men and 421 women; response rate: 95%). A structured questionnaire developed by the World Health Organization was administered online to all participants inquiring about a wide range of measures and practices employed at health facility level for an effective IPC approach. Fisher’s exact test was used to assess potential urban-rural differences in the distribution of characteristics regarding IPC aspects reported by survey participants. Results: About 47% of health facilities did not have a designated focal point for IPC issues; the lack of one patient per bed standard was evident in more than one-third of health facilities (37%); and the lack of an adequate distance between patient beds was reported in a quarter of health facilities (which was twice as high among health facilities in urban areas compared to rural areas). Furthermore, water services were always available only in about two-thirds of health facilities (63%), whereas an adequate number of toilets (at least two) was evident in slightly more than half of the health facilities surveyed (53%). Also, one out of four of the health facilities did not have functional hand hygiene stations and/or sufficient energy/power supply. A completely adequate ventilation was evidenced in slightly more than half of the health facilities (51%). Four out of five health facilities had always available materials for cleaning and about half (49%) had always available personal protective equipment. Functional waste collection containers were available in nine out of ten health facilities, of which, four out of five were correctly labelled. Conclusion: This study informs about the existing structures, capacities and available resources regarding IPC situation in different health facilities in Albania. Policymakers and decision-makers in Albania and in other countries should prioritize investments regarding IPC aspects in order to meet the basic requirements and adequate standards in health facilities at all levels of care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".