Gauging Alignments: An Ethnographically Informed Method for Process Evaluation in a Community-Based Intervention
Bibliographic record
Abstract
Abstract: Community-based projects feature multidimensional interventions and interactions within unpredictable contexts. Process evaluations can shed light on variability in outcomes across sites and the reasons why some project outcomes fall short of expectations. The authors present an ethnographically informed study of the interactive project components in a pilot community-based falls prevention project that was implemented in 4 communities across Canada. Ethnographic descriptions and analyses of alignments between multilevelled project components allowed the researchers to better understand the mechanisms of project evolution at each site and variations in project momentum, mobilization, and sustainability across sites. Primary data sources consisted of project teleconference transcripts triangulated with log notes, field notes, and interviews. Descriptions and analyses of alignments may be instrumental to process evaluation. Project adjustments could then be made accordingly in propelling progress toward program objectives, informing program decisions, and in making sense of variability in program outcomes. Further exploration and operationalization of the alignment concept is recommended to advance knowledge about how to conduct process evaluations of complex interventions.
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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 | 0.099 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".