Developing a Theoretical Framework for Complex Community-Based Interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Applying existing theories to research, in the form of a theoretical framework, is necessary to advance knowledge from what is already known toward the next steps to be taken. This article proposes a guide on how to develop a theoretical framework for complex community-based interventions using the Cardiovascular Health Awareness Program as an example. Developing a theoretical framework starts with identifying the intervention's essential elements. Subsequent steps include the following: (a) identifying and defining the different variables (independent, dependent, mediating/intervening, moderating, and control); (b) postulating mechanisms how the independent variables will lead to the dependent variables; (c) identifying existing theoretical models supporting the theoretical framework under development; (d) scripting the theoretical framework into a figure or sets of statements as a series of hypotheses, if/then logic statements, or a visual model; (e) content and face validation of the theoretical framework; and (f) revising the theoretical framework. In our example, we combined the "diffusion of innovation theory" and the "health belief model" to develop our framework. Using the Cardiovascular Health Awareness Program as the model, we demonstrated a stepwise process of developing a theoretical framework. The challenges encountered are described, and an overview of the strategies employed to overcome these challenges is presented.
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.
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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 it