Gauging alignments : an ethnographyically informed method for process evaluation in a community-based intervention
Bibliographic record
Abstract
Community-based projects feature multidimensional interventions \nand interactions within unpredictable contexts. Process \nevaluations can shed light on variability in outcomes across sites \nand the reasons why some project outcomes fall short of expectations. \nThe authors present an ethnographically informed study \nof the interactive project components in a pilot community-based \nfalls prevention project that was implemented in 4 communities \nacross Canada. Ethnographic descriptions and analyses of \nalignments between multilevelled project components allowed \nthe researchers to better understand the mechanisms of project \nevolution at each site and variations in project momentum, mobilization, \nand sustainability across sites. Primary data sources \nconsisted of project teleconference transcripts triangulated with \nlog notes, field notes, and interviews. Descriptions and analyses \nof alignments may be instrumental to process evaluation. \nProject adjustments could then be made accordingly in propelling \nprogress toward program objectives, informing program decisions, \nand in making sense of variability in program outcomes. Further \nexploration and operationalization of the alignment concept is \nrecommended to advance knowledge about how to conduct process \nevaluations 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 | 0.085 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".