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Record W4382399827 · doi:10.1177/20543581231183369

North American Weight Management Programs for People Living With Chronic Kidney Disease: An Environmental Scan

2023· article· en· W4382399827 on OpenAlexaffabout
Amani Hamadi, Kristin K. Clemens, Jaclyn Ernst, David Attalla, Louise Moist

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsSt Joseph's Health CareLawson Health Research InstituteInstitute for Clinical Evaluative SciencesWestern University
Fundersnot available
KeywordsWeight managementMedicineWeight lossKidney diseasePopulationObesityGerontologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Background: The availability and accessibility of patient-centered weight management programs is critical to mitigate the increasing prevalence of obesity in people living with chronic kidney disease (CKD). Little is known about the availability of contemporary programs that can safely and effectively support individuals living with obesity and CKD across North America. Objective: We sought to identify weight management programs specific to those with CKD and explore their safety, affordability, and adaptability to this patient population. We also identified the barriers and facilitators of identified programming including their accessibility to real-world patients (eg, cost, access, support, and time). Design: Environmental scan of weight management programs. Setting: North America. Patient: People living with CKD. Methods: We identified weight management programs and associated barriers and facilitators, via an Internet-based search of commercial, community-based, and medically supervised weight management programming. We also conducted a gray literature search and contacted weight management experts and program facilitators to explore strategies as well as their barriers and facilitators. Results: We identified 40 weight management programs available to people living with CKD across North America. Programs were commercial (n = 7), community-based (n = 9), and medically supervised (Canada n = 13, U.S n = 8) in origin. Three programs were specifically tailored to CKD (n = 3). In addition to formal programs, we also identified online nutritional resources and guidelines for weight loss in CKD (n = 8), and additional strategies (self-management tools, group orientated programs, moderate energy restrictions in conjunction with exercise and Orlistat) for weight loss from the gray literature (n = 3). Most common barriers were difficulty accessing some of the suggested nutritious food options due to the high cost, lack of support from family, friends and health practitioners, the time commitment required to participate, and the exclusion from weight management programs due to unique dietary needs for the CKD population. Most common facilitators were programs that were patient-centered, evidence-based, and offered both group and individual formats. Limitations: Although our search criteria were broad, it is possible that we did not capture all weight management programs available across North America. Conclusions: This environmental scan has generated a resource list of existing safe and effective programs for or adaptable to people with CKD. This information will inform future efforts to develop and deliver CKD-specific weight management programs to patients living with comorbid disease. Engaging people living with CKD to understand the acceptability of these programs, is an important focus for future research.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.008
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.009
GPT teacher head0.246
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2023
Admission routes2
Has abstractyes

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