The Diabetes Evaluation Framework for Innovative National Evaluations (DEFINE): Construct and Content Validation Using a Modified Delphi Method
Bibliographic record
Abstract
OBJECTIVES: In order to scale-up successful innovations, more evidence is needed to evaluate programs that attempt to address the rising prevalence of diabetes and the associated burdens on patients and the healthcare system. This study aimed to assess the construct and content validity of the Diabetes Evaluation Framework for Innovative National Evaluations (DEFINE), a tool developed to guide the evaluation, design and implementation with built-in knowledge translation principles. METHODS: A modified Delphi method, including 3 individual rounds (questionnaire with 7-point agreement/importance Likert scales and/or open-ended questions) and 1 group round (open discussion) were conducted. Twelve experts in diabetes, research, knowledge translation, evaluation and policy from Canada (Ontario, Quebec and British Columbia) and Australia participated. Quantitative consensus criteria were an interquartile range of ≤1. Qualitative data were analyzed thematically and confirmed by participants. An importance scale was used to determine a priority multi-level indicator set. Items rated very or extremely important by 80% or more of the experts were reviewed in the final group round to build the final set. RESULTS: Participants reached consensus on the content and construct validity of DEFINE, including its title, overall goal, 5-step evaluation approach, medical and nonmedical determinants of health schematics, full list of indicators and associated measurement tools, priority multi-level indicator set and next steps in DEFINE's development. CONCLUSIONS: Validated by experts, DEFINE has the right theoretic components to evaluate comprehensively diabetes prevention and management programs and to support acquisition of evidence that could influence the knowledge translation of innovations to reduce the burden of diabetes.
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 | 0.322 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".