A culturally informed lower-extremity complication prevention program for people living with diabetes in south India
Bibliographic record
Abstract
BACKGROUND: Diabetes and its complications are increasing in frequency worldwide. Lower-extremity complications carry a high risk for morbidity and mortality, yet are largely preventable through education and self-monitoring. In India, rural areas lack access to education, care, and treatment. Despite existing evidence-based programs to reduce diabetes-related lower-extremity complications in areas with limited resources, uptake and sustainability may be hampered by the lack of translation to the local cultural context. AIMS: To address this gap, this study used the Culturally Informed Healthy Aging nursing process to develop a lower extremity complication prevention program in a rural village. The paper describes the results of a community health needs assessment conducted annually from 2009 to 2014, and subsequent pilot test of an intervention incorporating these results. METHODS: The Culturally Informed Healthy Aging process is a naturalistic, inductive method used to identify and address health needs. Components include community partnership, community assessment, program planning, selection of health priorities, workgroup formation and translation of evidence, and program outcome evaluation. The programming is assessed using process evaluation, which allows for continuous monitoring and program modification. RESULTS: Community assessment revealed a number of values, beliefs, and practices related to foot care and assessment in rural south India. These were incorporated into culturally informed programming and evidence-based protocols were adapted for use in the local context. Programming resulted in increased community capacity for lower extremity complication prevention, accessible population screening, and culturally informed foot care education. DISCUSSION: Strengths, limitations and implications for care in rural India and other areas are discussed.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".