Modelling patient decision-making among Manitobans in the context of type II diabetes mellitus (T2DM)
Bibliographic record
Abstract
This thesis documents the development and implementation of a dynamic, stochastic model of decision-making in the context of type 2 diabetes mellitus (T2DM), with specific reference to the Province of Manitoba. The second chapter (i.e., after the introduction) informs development of the model by summarizing the state of knowledge regarding T2DM and surveying existing economic models and frameworks. T2DM is a chronic condition that can reduce life expectancy, adversely affect labour market performance, and profoundly impact quality of life. People can reduce their risk of T2DM and manage its progression in various ways but exert limited control over health outcomes. Within this context, this thesis studies optimal decision-making by fully-rational, forward-looking individuals by extending Peter Zweifel, Friedrich Breyer, and Mathias Kifmann’s model of health production and demand. The third chapter conceptualizes health-related decision-making as a dynamic optimization problem in which health and economic events constitute transitions between predefined states. T2DM and its complications detract from quality of life, increase mortality risk, and restrict opportunities to engage in consumption and leisure. People can reduce, but not eliminate, the chance of developing T2DM and the rate of diabetic progression through regular physical activity and adherence to healthy eating habits and pharmacotherapy, but only by sacrificing time and income that could be allocated to other things. The model cannot be solved analytically but is amenable to application of numerical techniques. The remainder of Chapter 3 selects values or ranges for model parameters to reflect the circumstances in which Manitobans make health-related decisions. The fourth chapter describes model implementation, interprets key results, and illustrates potential applications. Fully-rational, forward-looking individuals are generally motivated to invest in health, but this is mediated by their circumstances and personal characteristics, and they may sometimes trade off increased risk of adverse health outcomes or accelerated rate of diabetic progression in return for more consumption or leisure. Microsimulations based on these results suggest opportunities to reduce efficiency losses attributable to T2DM in Manitoba through improved health-related decision-making, but this requires investing in both T2DM prevention and management, and may entail altering the structure of incentives within which choices are made.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".