Adapting realist synthesis methodology: The case of workplace harassment interventions
Bibliographic record
Abstract
Realist synthesis techniques can be used to assess complex interventions by extracting and synthesizing configurations of contexts, mechanisms, and outcomes found in the literature. Our novel and multi-pronged approach to the realist synthesis of workplace harassment interventions describes our pursuit of theory to link macro and program level theories. After discovering the limitations of a dogmatic approach to realist synthesis, we adapted our search strategy and focused our analysis on a subset of data. We tailored our realist synthesis to understand how, why, and under what circumstances workplace harassment interventions are effective. The result was a conceptual framework to test our theory-based interventions and provide the basis for subsequent realist evaluation. Our experience documented in this article contributes to an understanding of how, under what circumstances, and with what consequences realist synthesis principles can be customized.
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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 | 0.287 | 0.375 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".