Food Insecurity Determinants, Coping Strategies, and Association With Nutritional Status Among Hemodialysis Patients in Pahang, Malaysia: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Food insecurity involves the lack of physical and economic access to sufficient, safe, and nutritious food that meets an individual's dietary needs. Patients with end-stage renal disease (ESRD) undergoing hemodialysis have specific dietary needs, but food insecurity may hinder them from adhering to prescribed guidelines and preserving their nutritional status. However, no research has been conducted to elucidate food insecurity among patients undergoing hemodialysis in Malaysia. OBJECTIVE: This study aimed to assess the prevalence of food insecurity, its determinants, and its association with nutritional status and explore the coping strategies used among patients undergoing hemodialysis in Pahang, Malaysia. METHODS: This was a cross-sectional study that followed a mixed methods approach and was conducted with patients undergoing hemodialysis for ESRD at the Pahang Islamic Religious Council and Malay Customs (Majlis Ugama Islam dan Adat Resam Melayu Pahang [MUIP]) dialysis centers. The inclusion criteria were patients who were aged 18 years or older, were generally healthy, and had been undergoing hemodialysis regularly for at least 3 months. The food security status of the participants was determined using the Malay version of the Food Insecurity Experience Scale (M-FIES). The nutritional status included anthropometric measurements (height, weight, BMI, triceps skinfold [TSF] thickness, mid-upper arm circumference (MUAC), midarm muscle circumference (MAMC), and body fat percentage), biochemical parameters (serum urea, creatinine, albumin, phosphate, potassium, hemoglobin, and total iron-binding capacity [TIBC]), clinical assessments (Malnutrition Inflammation Score [MIS] and protein energy wasting [PEW]), and dietary intake (adherence to total calorie, protein, sodium, potassium, and phosphorus intake and diet monotony index [DMI]). The determinants were identified using logistic regression, while the association between food security status and nutritional status was analyzed using the chi-square or Fisher's exact test and the independent t test. Semistructured interviews involved participants who were categorized as mildly, moderately, or severely food insecure in order to explore the contributing factors of food insecurity and how they coped with it based on their lived experience. The interviews were carried out until the data reached saturation, and then the data were analyzed thematically. RESULTS: The study was funded by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme (FRGS/1/2023/SS10/UIAM/02/1) starting in September 2023. Data collection was conducted from December 2023 until August 2024, involving 287 participants, and data analysis has also been completed. As of January 2026, quantitative findings are under review and qualitative findings are being prepared. CONCLUSIONS: The implementation of this study protocol will provide new evidence to improve the understanding of food insecurity in this population. Elucidation of its key contributing factors, coping strategies, and potential connections to nutritional status in this population can help guide more informed policymaking and effective interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/84575.
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 | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".