Barriers and Problems in Implementing Health-Associated Infections Surveillance Systems in Iran: A Qualitative Study
Bibliographic record
Abstract
Background: Healthcare-associated infections (HAIs) are among the most critical challenges for patients and healthcare providers. To achieve the goals of the surveillance system, it is necessary to identify its barriers and problems. This study aimed to identify the barriers and problems of the surveillance system for HAIs. Methods: This qualitative study was conducted using the content analysis method to investigate the challenges of this surveillance system from the perspective of 18 infection control nurses from hospitals in different cities of Iran with work experience of 1 to 15 years. Data were collected through semi-structured interviews and analyzed using the Lundman and Graneheim qualitative content analysis method. Results: In this study, we found 2 categories and 7 subcategories. Two categories were barriers related to human resources and organizational barriers to infection control. The 7 subcategories included weakness of medical staff in adherence to health principles, obstacles related to patients, high workload and insufficient motivation, lack of staff knowledge, lack of human resources, functional and logistical weaknesses, and weaknesses in the surveillance system. Conclusion: To reduce problems and improve HAIs reporting, the HAIs surveillance system needs the support of health system officials and managers. This administrative and support focus can establish the framework for removing and lowering other barriers, such as the number of reported cases, physician and staff noncooperation, and the prevalence of HAIs. It can also bring HAIs cases closer to reality.
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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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".