Conceptual tensions and practical trade-offs in tailoring implementation interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it