Assessment of Dietary Practices among Type 2 Diabetes Attending Primary Health Care Centres in Kirkuk City
Bibliographic record
Abstract
Good dietary practices are an important ingredient in managing type 2 diabetes. This study assesses dietary practices among type 2 diabetes attending primary health care centres in Kirkuk City. The descriptive approach was used to study dietary practices for people with type 2 diabetes. The study was carried out on a total of 120 persons with type 2 diabetes of both sexes, 58 males and 62 females, attending the primary health care centers. To meet the study's goals, the sample was chosen using a purposive sample (non-probability), which was carried out in primary health centres in the first and second sectors in Kirkuk City, Iraq. Data was gathered using the interview technique for each participant from (2nd March up to 2nd August 2023, by utilizing a structured questionnaire. Version 22 of Statistical Package for Social Sciences (SPSS) was used to analyze the data that were gathered. The results of the study revealed that out of all participants, only 20.8% had good dietary practices, 72.5% had moderate dietary practices, and 6.7% had poor levels of dietary practices. There were statistically significant differences in the dietary practices of diabetic patients according to age, knowledge about diabetic healthy nutrition, consulting a dietician, and education level of patients. According to the results concluding, less than a quarter of diabetic patients had good dietary practices. There are differences in dietary practices attributable to variables (age, educational levels, knowledge about diabetic nutrition, and consulting a dietician). Therefore, good dietary practices among diabetic patients require improvement due to inadequacy.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".