Improving intervention development and implementation using the Person-Based Approach (PBA) to co-participatory stakeholder engagement: a worked example of a behavioural intervention to improve asthma reviews in primary care
Bibliographic record
Abstract
Abstract Backgrounds: New interventions need to fit with existing ways of working in primary care. The Person-Based Approach (PBA) is a way to tailor interventions to context and stakeholder engagement can be a more or lesser part of this approach. Using co-participatory stakeholder engagement, as part of the PBA, provides a novel way to involve clinicians and PPI representatives in intervention development to maximise the acceptability and fine tune implementation of the intervention. Methods: A behavioural intervention was developed using the PBA, emphasizing co-participatory stakeholder engagement of clinicians and PPI contributors. We developed an online intervention to support the use of a Fractional Exhaled Nitric Oxide (FeNO) test to guide clinical decisions during routine asthma reviews in primary care. Decisions about intervention planning and development were made through regular meetings and interactions with patients with asthma and primary care clinicians using the intervention. Results: A varied group of stakeholders were involved, including GPs, practice nurses, clinical pharmacists, patients with asthma and academics in primary care and respiratory research. Including active stakeholder engagement throughout the intervention development process enabled better understanding of the context in which primary care asthma reviews happen, the specific needs of patients with asthma and clinicians conducting reviews and how to best meet these needs to increase the acceptability of the intervention and fit with practice. Stakeholder feedback also identified necessary changes to intervention materials, which would not have been identified by the research team alone. Discussion: This working example provides insights on how stakeholder engagement complemented and strengthened research activities and provides a model for understanding how best to utilise the feedback received by stakeholders to maximise adoption of interventions and their implementation in practice.
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 | 0.092 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".