Designing theoretically-informed implementation interventions: Fine in theory, but evidence of effectiveness in practice is needed
Bibliographic record
Abstract
The Improved Clinical Effectiveness through Behavioural Research Group (ICEBeRG) authors assert that a key weakness in implementation research is the unknown applicability of a given intervention outside its original site and problem, and suggest that use of explicit theory offers an effective solution. This assertion is problematic for three primary reasons. First, the presence of an underlying theory does not necessarily ease the task of judging the applicability of a piece of empirical evidence. Second, it is not clear how to translate theory reliably into intervention design, which undoubtedly involves the diluting effect of "common sense." Thirdly, there are many theories, formal and informal, and it is not clear why any one should be given primacy. To determine whether explicitly theory-based interventions are, on average, more effective than those based on implicit theories, pragmatic trials are needed. Until empirical evidence is available showing the superiority of theory-based interventions, the use of theory should not be used as a basis for assessing the value of implementation studies by research funders, ethics committees, editors or policy decision makers.
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.
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 | 0.088 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.062 | 0.086 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".