Applying the Behaviour Change Wheel to support deprescribing in long-term care: Qualitative interviews with stakeholder participants
Bibliographic record
Abstract
Background: Implementation and behavioural science are increasingly being used to support development and translation of evidence-based interventions into practice. We used the Behaviour Change Wheel (BCW) approach in two stakeholder forums to identify target behaviours and supporting actions to inform the development of a framework to support deprescribing in long-term care homes. During our planning for these forums, we found many applications of the BCW approach used in healthcare. However, we found no accounts of stakeholders' experiences when the BCW approach was used with large groups of people who were mostly unfamiliar with behavioural science. Objective: The goal of this research was to gain insight into the use of the BCW approach in the context of developing a framework to support deprescribing in long-term care. Methods: This descriptive qualitative study employed one-on-one semi-structured interviews with Ontario long-term care stakeholders who had participated in one or both of two in-person forums that we hosted. Interviews were transcribed verbatim and an inductive content-analysis approach was used to code data and determine themes. Results: Fifteen interviews were conducted. Four themes were identified. First, the BCW was new and made sense, but people found it hard to identify target behaviours before planning solutions. Second, participants varied in their opinions as to whether the 'right' people were participating. Third, participants found that the forum activities, worksheets and facilitators helped people use the approach. Fourth, stakeholder perspectives about potential implementation challenges and strategies to maximize success were identified. Conclusions: Overall, participants were positive about the use of the BCW approach, however, its usefulness could be optimized by enhancing explanations, facilitation and logistics to ensure an initial focus on targeting behaviours. Making stakeholder perspectives transparent and ensuring mechanisms are present to ensure all views are sought and considered are also important to optimizing participant experience.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.033 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".