Choosing and Using Theories in Diabetes Education Research
Bibliographic record
Abstract
Diabetes educators use theories all the time, even if they are not aware of it. To teach, one must have some assumptions about how people learn and what constitutes effective teaching. The purpose of this article is to help diabetes educators interested in research and evaluation choose appropriate theories. The article will review the 4 purposes of theories, that is, description, explanation, prediction, and control, as well as the degree to which a theory has been articulated and elaborated. The importance of a theory's personal resonance, its explanatory power, and its utility will also be examined. The article will also review how to use 1 or more theories at each stage of a research or evaluation project.
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 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 | 0.351 | 0.296 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.012 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.024 | 0.030 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".