Shared decision-making and behaviour change collide: an analysis of consultations discussing clinical trial recruitment
Bibliographic record
Abstract
BACKGROUND: Recruitment to clinical trials is often challenging, and evidence on effective recruitment strategies remains limited. Framing trial recruitment as a behaviour, amenable to change, enables the application of behavioural science frameworks to better understand and potentially improve recruitment processes. Since trial participation is also a preference-sensitive decision, shared decision-making (SDM) may further enhance ethical recruitment. The present study aimed to explore how behavioural taxonomies and SDM frameworks can be applied to recruitment consultations to identify behaviour change techniques (BCTs) and assess the extent of SDM. METHODS: A secondary analysis was conducted on 53 audio-recorded consultations; consultations from 3 trials in oncology and gastroenterology were sampled. The action, actor, context, target, and time (AACTT) framework was used to define recruitment behaviours, which were then coded using the Behaviour Change Technique Taxonomy v1 (BCTTv1). SDM was assessed using the 5-item 'Observing Patient Involvement' (OPTION5) tool, which evaluates patient involvement in decision-making. Descriptive statistics and frequency counts were used to analyse the data. RESULTS: Twenty-one of the 93 BCTs in the BCTTv1 were identified across all consultations. The most frequently coded BCTs were 5.1 (information about health consequences) and 5.3 (information about social and environmental consequences), both present in all consultations. No substantial difference in the total number of BCTs was observed between trial consenters (M = 6.8, SD = 1.5) and decliners (M = 6.4, SD = 2.1). However, some techniques showed variability: BCT 7.1 (prompts and cues) appeared more frequently in consultations with consenters (84%) than decliners (38%), while BCT 1.4 (action planning) was more frequent in decliners (44%) than consenters (27%). SDM, as measured by OPTION5, was low overall, with a mean score of 27.7 (SD = 12.2) out of 100, with no significant differences across trials or participant groups. CONCLUSIONS: This study demonstrates the feasibility of applying behavioural science and SDM frameworks to analyse trial recruitment consultations. While a range of BCTs were identified, SDM efforts were generally low. Recruitment practices may benefit from more deliberate consideration of these techniques and greater emphasis on shared decision-making to support informed choices.
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How this classification was reachedexpand
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 | 0.034 | 0.238 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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; both teacher heads 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".