Exploring barriers and enablers to the delivery of Making Every Contact Count brief behavioural interventions in Ireland: A cross‐sectional survey study
Bibliographic record
Abstract
Abstract Objectives The public health impact of the Irish Making Every Contact Count (MECC) brief intervention programme is dependent on delivery by health care professionals. We aimed to identify enablers and modifiable barriers to MECC intervention delivery to optimize MECC implementation. Design Online cross‐sectional survey design. Methods Health care professionals ( n = 4050) who completed MECC eLearning were invited to complete an online survey based on the Theoretical Domains Framework (TDF). Multiple regression analysis identified predictors of MECC delivery (logistic regression to predict delivery or not; linear regression to predict frequency of delivery). Data were visualized using Confidence Interval‐Based Estimates of Relevance (CIBER). Results Seventy‐nine per cent of participants ( n = 283/357) had delivered a MECC intervention. In the multiple logistic regression (Nagelkerke's R 2 = .34), the significant enablers of intervention delivery were ‘professional role’ (OR = 1.86 [1.10, 3.15]) and ‘intentions/goals’ (OR = 4.75 [1.97, 11.45]); significant barriers included ‘optimistic beliefs about consequences’ (OR = .41 [.18, .94]) and ‘negative emotions’ (OR = .50 [.32, .77]). In the multiple linear regression ( R 2 = .29), the significant enablers of frequency of MECC delivery were ‘intentions/goals’ ( b = 10.16, p = .02) and professional role ( b = 6.72, p = .03); the significant barriers were ‘negative emotions’ ( b = −4.74, p = .04) and ‘barriers to prioritisation’ ( b = −5.00, p = .01). CIBER analyses suggested six predictive domains with substantial room for improvement: ‘intentions and goals’, ‘barriers to prioritisation’, ‘environmental resources’, ‘beliefs about capabilities’, ‘negative emotions’ and ‘skills’. Conclusion Implementation interventions to enhance MECC delivery should target intentions and goals, beliefs about capabilities, negative emotions, environmental resources, skills and barriers to prioritization.
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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 | 0.022 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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; a candidate call from one teacher head, 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".