A university and community partnership that built capacity through program evaluation
Bibliographic record
Abstract
A community organization-university partnership was formed to complete program evaluation research to enhance the effectiveness of the organization in its efforts to improve the resiliency of families in crisis or in need of respite. Incorporating the voices of staff and former families who had the critical expertise and lived experiences with programs and evaluative tools undergirded this work. Six themes arose from focus groups and interview data that provided recommendations for leadership on the evaluative process and tools used and informed the literature review. The theoretical approach to this research highlighted program inequities and illuminated the need for more culturally safe program evaluation practices that respect diversity and inclusivity and focus on equity-building designs. Although time-consuming, front-line staff became familiar with their own program logic models, understood how they connected to their day-to-day work with children and families, and developed a sense of ownership through hands-on involvement. Importantly, logic model development was demystified. Recognizing the organization needed a less intimidating visual representation of their logic models, a one-page version was developed for each program along with fuller versions. A repository of measures developed for staff will ensure ongoing access to evidence-informed tools for updating the evaluative framework for their programs.
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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 | 0.062 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".