The Evaluation of Equity-Focused Community Coalitions: A Review of the Empirical Literature
Bibliographic record
Abstract
Conducting evaluation and research with community coalitions involved in health equity initiatives is inherently complex. In this paper we provide a review and synthesis of the empirical literature on the evaluation of equity-focused community coalitions. We explore issues, challenges, and barriers experienced by evaluators, as well as techniques and approaches that were considered beneficial. Our review identified 11 peer reviewed articles, from which we identified seven overlapping themes: (1) framing equity in the evaluation process, (2) use of multiple theoretical frameworks, (3) use of systems-focused approaches, (4) strategic use of intersectoral partnerships and collaborations, (5) intentional communication and building trusting relationships, (6) challenges dedicating purposeful time to the work, and (7) issues of cultural and contextual clarity and responsiveness. Our findings point to a significant focus on context, history, learning, communication, relationships, and power. The cultural complexity and historical scope of each context, diversity of stakeholders, and enormity of the systemic issues involved, shape and challenge the evaluation and research process in fundamental ways, requiring a creative and kinetic thinking -- a shifting from methodological certainty to an acknowledged uncertainty, where mixing, blending and the innovative use of approaches and theories becomes a way of moving beyond the colonizing past.
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 | 0.061 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".