Testing a prediabetes screening approach for a Latin American population in Vancouver, Canada
Bibliographic record
Abstract
otero lm, fong m, papineau d, thorne s & zanetti ml (2011) Journal of Nursing and Healthcare of Chronic Illness3, 329–338 Testing a prediabetes screening approach for a Latin American population in Vancouver, Canada Aim. To determine whether the CANRISK Diabetes Risk Assessment tool can be a useful component of a screening programme to identify risk for developing diabetes mellitus type 2 (DM) in a Latin American immigrant population in a Canadian urban health service region. Background. Diabetes mellitus type 2 prevalence is rapidly increasing and has been identified as a population health priority. Immigrants from Latin American countries are among the higher risk ethnic groups within the diverse Canadian urban population. Method. Within a larger multi-site project to validate the Public Health Agency of Canada’s CANRISK Questionnaire, we studied a convenience sample of 44 Spanish- and Portuguese-speaking Latin Americans to assess its utility as a potential component of a prediabetes risk screening approach with this population. Using a cross-sectional exploratory design, we compared CANRISK questionnaire results with values derived from controlled blood glucose testing. Data were collected from 2009–2010. Results. CANRISK assessment was readily accepted within this population when administered in study participants’ native language. Laboratory testing detected abnormal fasting plasma glucose (FPG) values in 4·7% of this population, 4·5% with abnormal oral glucose tolerance test (OGTT) in 4·5% and abnormal haemoglobin A1c values in 9·1%. In contrast, the CANRISK tool identified 11·4% of the sample to be at high risk, 9·1 at moderate risk and 43·2% at slightly elevated risk for developing DM. Conclusion. CANRISK identified candidates who might benefit from risk reduction interventions in whom biological indices typically signalling the need for attention were not yet apparent. It is easily administered with this higher risk population, and may be useful to identify a significantly wider spectrum of prediabetes risk than can be detected clinically. Relevance to clinical practice. Nurses may consider using the CANRISK questionnaire to detect DM risk within this population as an adjunct to prevention strategies aimed at reducing the incidence and prevalence of this disease.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".