Examining the complementarity between the ERIC compilation of implementation strategies and the behaviour change technique taxonomy: a qualitative analysis
Bibliographic record
Abstract
BACKGROUND: Efforts to generate evidence for implementation strategies are frustrated by insufficient description. The Expert Recommendations for Implementing Change (ERIC) compilation names and defines implementation strategies; however, further work is needed to describe the actions involved. One potentially complementary taxonomy is the behaviour change techniques (BCT) taxonomy. We aimed to examine the extent and nature of the overlap between these taxonomies. METHODS: Definitions and descriptions of 73 strategies in the ERIC compilation were analysed. First, each description was deductively coded using the BCT taxonomy. Second, a typology was developed to categorise the extent of overlap between ERIC strategies and BCTs. Third, three implementation scientists independently rated their level of agreement with the categorisation and BCT coding. Finally, discrepancies were settled through online consensus discussions. Additional patterns of complementarity between ERIC strategies and BCTs were labelled thematically. Descriptive statistics summarise the frequency of coded BCTs and the number of strategies mapped to each of the categories of the typology. RESULTS: Across the 73 strategies, 41/93 BCTs (44%) were coded, with 'restructuring the social environment' as the most frequently coded (n=18 strategies, 25%). There was direct overlap between one strategy (change physical structure and equipment) and one BCT ('restructuring physical environment'). Most strategy descriptions (n=64) had BCTs that were clearly indicated (n=18), and others where BCTs were probable but not explicitly described (n=31) or indicated multiple types of overlap (n=15). For some strategies, the presence of additional BCTs was dependent on the form of delivery. Some strategies served as examples of broad BCTs operationalised for implementation. For eight strategies, there were no BCTs indicated, or they did not appear to focus on changing behaviour. These strategies reflected preparatory stages and targeted collective cognition at the system level rather than behaviour change at the service delivery level. CONCLUSIONS: This study demonstrates how the ERIC compilation and BCT taxonomy can be integrated to specify active ingredients, providing an opportunity to better understand mechanisms of action. Our results highlight complementarity rather than redundancy. More efforts to integrate these or other taxonomies will aid strategy developers and build links between existing silos 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 | 0.109 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".