Teachers’ knowledge about communicable disease control at primary schools in Mosul City: A cross-sectional study
Bibliographic record
Abstract
Introduction: In emergency situations, particularly complicated ones including the collapse of health services and disease control programs, a major cause of mortality and morbidity is limited access to health care and communicable diseases. The aim of the study is to assess the primary school teachers’ knowledge of communicable illness control in Mosul City. Materials and Methods: A descriptive cross-sectional study design was employed, utilizing a purposeful sample of 149 teachers. The study was carried out at Mosul City primary schools in the first quarter of 2024. A survey was made specifically for this purpose. It was made up of two parts. The data were analyzed using SPSS version 26. Results: Findings of the current study showed that 7 (4.7%) teachers only had excellent knowledge of communicable disease prevention and control, while the majority of teachers, 105 (70.5%), had poor knowledge of these topics. Conclusion: The majority of elementary school teachers in Mosul City lack sufficient information on managing communicable diseases. The study advised the essential to work with the Ministry of Health and Environment to implement educational programs on communicable diseases and preventive measures to raise their level of awareness regarding the prevention and control of communicable illnesses. The following core competencies are addressed in this article: Medical knowledge, Practice-based learning and improvement, Systems-based practice.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".