Creating Logic Models Using Grounded Theory: A Case Example Demonstrating a Unique Approach to Logic Model Development
Bibliographic record
Abstract
Abstract: This article describes, using a case example, the procedure of creating logic models using grounded theory methodology in the context of process evaluation. There currently exists a dearth of literature on the specifics of how logic models should be created. The authors reduce this gap by detailing an integrated methodology they utilized during their recent evaluation of the Youth Educating About Health (YEAH) program. A number of parallels between grounded theory and logic modelling are first discussed to demonstrate their potential for integration. Then the data collection and analysis procedures are explained with a focus on how the integration between grounded theory and logic modelling was conducted. The completed logic model is then presented and each category is explained in detail. The authors conclude by discussing the lessons they learned from utilizing this integrated methodology. These lessons include the specific benefits this methodology contributes to process evaluation, the added depth of information that grounded theory provides to logic modelling, and the cost- and time-effectiveness of this unique methodology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".