Increasing diabetes testing adherence with incentives in rural Northwestern Ontario
Bibliographic record
Abstract
Introduction: The health outcomes of rural Canadians have been described as poor and can in some part be related to diabetes mellitus. Despite the high mortality and morbidity rates associated with the disease, compliance with management remains low. Research has shown that a small financial incentive used to modify patient behaviour, can improve outcomes in cardiac disease and exercise adherence. This study aims to evaluate if a small financial incentive awarded to rural Northwestern Ontario patients with diabetes who complete an haemoglobin A1c (HbA1c) test, would result in greater compliance in test completion. Methods: Patients were recruited through two Northern rural clinics. Participants were divided into two groups: Group A received a financial incentive, whereas Group B received a letter of reminder. HbA1c tests were recorded every 6 months for 2 years and compliance was analysed using a t-test and Chi-square. Results: One hundred and forty-six participants were recruited with 30 lost to follow-up. Overall, the incentive group completed a statistically significantly higher number of HbA1c tests compared to those in the control group. In addition, it was noted that there was an increase in test adherence for participants that received reminder letters, although not an initially expected outcome of the study. Conclusion: The results suggest that either a financial incentive or a reminder directed towards rural Canadians could have a benefit in promoting health behaviours to subsequent medical management of diabetes mellitus.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".