Enhancing the implementation of the Making Every Contact Count brief behavioural intervention programme in Ireland: protocol for the Making MECC Work research programme
Bibliographic record
Abstract
Background: Brief behavioural interventions offered by healthcare professionals to target health behavioural risk factors (e.g. physical activity, diet, smoking and drug and alcohol use) can positively impact patient health outcomes. The Irish Health Service Executive (HSE) Making Every Contact Count (MECC) Programme supports healthcare professionals to offer patients brief opportunistic behavioural interventions during routine consultations. The potential for MECC to impact public health depends on its uptake and implementation. Aim: This protocol outlines the ‘Making MECC Work’ research programme, a HSE/Health Behaviour Change Research Group collaboration to develop an implementation strategy to optimise uptake of MECC in Ireland. The programme will answer three research questions: (1) What determines delivery of MECC brief interventions by healthcare professionals at individual and organisational levels? (2) What are patient attitudes towards, and experiences of, receiving MECC interventions from healthcare professionals? (3) What evidence-informed implementation strategy options can be consensually developed with key stakeholders to optimise MECC implementation? Methods: In Work Package 1, we will examine determinants of MECC delivery by healthcare professionals using a multi-methods approach, including: (WP1.1) a national survey of healthcare professionals who have participated in MECC eLearning training and (WP1.2) a qualitative interview study with relevant healthcare professionals and HSE staff. In Work Package 2, we will examine patient attitudes towards, and experiences of, MECC using qualitative interviews. Work Package 3 will combine findings from Work Packages 1 and 2 using the Behaviour Change Wheel to identify and develop testable implementation strategy options (WP 3.1). Strategies will be refined and prioritised using a key stakeholder consensus process to develop a collaborative implementation blueprint to optimise and scale-up MECC (WP3.2). Discussion: Research programme outputs are expected to positively support the integration of MECC brief behaviour change interventions into the Irish healthcare system and inform the scale-up of behaviour change interventions internationally.
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 | 0.083 | 0.090 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.106 | 0.024 |
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".