Knowledge, Attitude and Practice (KAP) related to Type 2 Diabetes Mellitus (T2DM) among Healthy Adults in Kiribati
Bibliographic record
Abstract
BACKGROUND: Type 2 Diabetes Mellitus (T2DM) kills more than 4.9 million adults yearly, and it is one of the major threats to global public health for low- and middle-income countries that mostly affects the adult population. Kiribati is currently facing the increasing prevalence of morbidity and mortality from T2DM. OBJECTIVE: To find out the level of Knowledge, Attitude and Practice (KAP) towards T2DM among healthy adults in South Tarawa, Kiribati. METHODS: This cross sectional study was conducted on South Tarawa, Kiribati at three randomly selected public health clinics from September 25 to November 20, 2017. Non-diabetic patients from both sexes who aged 18 years and above were selected by a simple random sampling technique to participate in this study. A pretested structured questionnaire was used to collect data and SPSS (version 22) was used for data analysis. Descriptive statistics was used to study the characteristics of the population and level of KAP. RESULTS: 405 person participated in this study. Majority of the participants were in the age range of 18-24 years (30.4%), were females (66.2%) and had ever married (68.6%). The study revealed that the mean knowledge score was 20.47 (±3.49) which shows that participants had moderate level of knowledge towards T2DM. The mean score for attitude score was 61.06 (±5.48) which shows that participants had high level of attitude towards T2DM. The mean practice score was 4.57 (±2.01) which shows that participants had a low level of practice towards T2DM. CONCLUSION: A great emphasis on health education regarding symptoms, risk factors modification and prevention is T2DM are necessary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".