Implementation of a national programme to train and support healthcare professionals in brief behavioural interventions: A qualitative study using the theoretical domains framework
Bibliographic record
Abstract
OBJECTIVES: Behaviour change interventions offered opportunistically by healthcare professionals can support patient health behaviour change. The Making Every Contact Count (MECC) programme in Ireland is a national programme to support healthcare professionals to use brief behavioural interventions. The aim of this study was to gain an in-depth understanding of the enablers of, and barriers to, embedding MECC across the healthcare system. DESIGN: A qualitative interview study. METHODS: We conducted individual semi-structured interviews to understand barriers and enablers to MECC implementation. Our sample was 36 participants (11 health promotion and improvement officers, 9 nurses, 15 allied health professionals and 1 training instructor) who have a direct role in either supporting or delivering brief interventions to patients. Data were analysed using a Framework Analysis approach guided by the Theoretical Domains Framework (TDF). RESULTS: Eight theoretical domains influenced MECC implementation: environmental context and resources, intentions/goals, beliefs about the consequences of MECC delivery, knowledge, healthcare professionals' beliefs about their capability to deliver MECC interventions, social and professional role and identity, and reinforcement and skills. Environmental context and resources was the most strongly endorsed domain with key influencing factors including consultation type/setting, making MECC a routine part of clinical practice, a multi-professional approach, access to/visibility of resources/services, management support/expectations, impacts of the COVID-19 pandemic, and the salience of the MECC programme and the strategic fit of MECC with other health service initiatives. CONCLUSIONS: While individual factors influence national implementation of behaviour change interventions, creating enabling environments for healthcare staff is crucial for widespread adoption across healthcare systems.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".