Gender Differentials In Nutritional Status of Elderly People In Selected Rural Areas of Bangladesh
Bibliographic record
Abstract
Background: The present cross sectional study was undertaken to assess the food habits and nutritional status among elderly people in rural Bangladesh and to compare the same between male and female. A simple random sampling and geographical re-conciliation method was used to select the study population. All the patients in a pre-publicized medical camp were approached and a total of 186 male and 237 female participated in the study. Data were collected through face to face interview with a semi-structured questionnaire and anthropometric measures were collected using instruments. Associations between dietary intake and World Health Organization (WHO) referred Body Mass Index (BMI) range was done using cross tabulation. Results: The mean age of male was 67.69 years and that of female was 65.46 years. The female subjects were higher than males in number in this study. The rate of male literacy was found to be 39.8% where literacy among the female was 13.9%. Among all, 79.6% males were found to be living with their spouse where the rate of living with spouse among the female amounted 53.2%. About half of elderly people were found to be living under poor and 32% in low middle class socio-economic condition. Study revealed that 80.6% male and 78.9% female got no opportunity to take protein-rich food more than three days per week. Again 95.7% male and 97.5% female had no opportunity take more than two servings of protein rich food per week. The similar case occurred in case of taking fatty food, vegetable and fruits per week. Similarly 100 % male and 99.6% female reported that they were taking more than two servings of fatty food per week. No significant association was found between BMI and food intake. The significance was tested by Pearson chi-square. In this test the p value for protein rich food was 0.234 (p?0.05), while fatty food (0.712), vegetable (0.502) and fruits (0.274) which was more than referred significance p-value ?0.05. Hence, the study confirmed that malnutrition remains a common problem among older people living in rural Bangladesh though there is no significant association was found between food intake and nutrition. Conclusion: Management of malnutrition in case of elderly population requires a multidisciplinary approach that treats pathology and uses both social and dietary forms of intervention. Bangladesh Journal of Medical Science Vol. 12 No. 02 April13 Page 150-157 DOI: http://dx.doi.org/10.3329/bjms.v12i2.14943
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".