Identifying behaviour change techniques within randomized trials of interventions promoting deceased organ donation registration
Bibliographic record
Abstract
OBJECTIVE: Increasing deceased organ donation registration may increase the number of available organs for transplant to help save lives. This study aimed to identify which behaviour change techniques (BCTs; or 'active ingredients') are reported within randomized trials of interventions promoting deceased organ donation registration and of those, which are associated with a larger intervention effect. METHODS: We conducted a secondary analysis of 45 trials included in a Cochrane systematic review of deceased organ donation registration interventions. Two researchers used the BCT Taxonomy v1 to independently code intervention content in all trial groups. Outcome data were pooled and we used meta-regression to explore associations between individual and combinations of recurring BCTs and effect on registration intention and/or registration behaviour. RESULTS: A total of 27 different BCTs (mean = 3.7, range = 1-9) were identified in intervention groups across the 45 trials. The five most common BCTs were: 'Information about health consequences' (71%); 'Instruction on how to perform the behaviour' (47%); 'Salience of consequences' (40%); 'Adding objects to the environment' (28%); and 'Credible source' (27%). Comparator groups in 20/45 trials also included identifiable BCTs (n = 12, mean = 3.1, range = 1-7). Meta-regression revealed that a combination of the three most common BCTs was associated with a larger intervention effect size for registration behaviour (k = 8, β = .19, p = .02). CONCLUSIONS: Trials of deceased organ donation registration interventions focus predominantly on providing information, instruction, and a means to register. While potentially effective, a much wider set of possible BCTs could be leveraged to address known barriers to registration.
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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 | 0.249 | 0.470 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".