Conceptual tensions and practical trade-offs in tailoring implementation interventions
Bibliographic record
Abstract
Tailored interventions have been shown to be effective and tailoring is a popular process with intuitive appeal for researchers and practitioners. However, the concept and process are ill-defined in implementation science. Descriptions of how tailoring has been applied in practice are often absent or insufficient in detail. This lack of transparency makes it difficult to synthesize and replicate efforts. It also hides the trade-offs for researchers and practitioners that are inherent in the process. In this article we juxtapose the growing prominence of tailoring with four key questions surrounding the process. Specifically, we ask: (1) what constitutes tailoring and when does it begin and end?; (2) how is it expected to work?; (3) who and what does the tailoring process involve?; and (4) how should tailoring be evaluated? We discuss these questions as a call to action for better reporting and further research to bring clarity, consistency, and coherence to tailoring, a key process in implementation science.
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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 | 0.534 | 0.505 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.011 | 0.129 |
| Scholarly communication | 0.032 | 0.055 |
| Open science | 0.012 | 0.022 |
| Research integrity | 0.020 | 0.020 |
| 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".