Realist evaluation: what works, for whom, and under what circumstances for recipients of Healthy Conversation Skills (HCS) training
Bibliographic record
Abstract
Making Every Contact Count (MECC) is a person-centred intervention that aims to utilise existing conversations that service providers have with service users to promote health behaviour change. It was initially implemented across NHS settings but has since been applied across a wide variety of settings including the voluntary, community, and social enterprise (VCSE) sector. MECC was a UK-based initiative but has since been applied in Ireland, Australia, South Africa, and Canada, and continues to be a popular national approach to health behaviour change: https://www.rsph.org.uk/our-work/policy/wider-public-health-workforce/what-are-you-talking-about.html. Approaches to MECC training vary considerably. Whilst some training approaches focus on encouraging brief advice, others encourage active listening, asking questions, and supporting goal setting. The broad applicability of MECC means that there are also a wide variety of recipients of MECC training, who have a broad range of different backgrounds and previous experience. Our previous work demonstrated that the specific MECC training approach called Healthy Conversation Skills (HCS) training, which supports service users in identifying their own solutions as opposed to providing advice, appears to be the most acceptable approach to MECC delivery in VCSE settings. Multiple previous studies have demonstrated that HCS training increases the confidence and ability of trainees to deliver MECC. However, the available evidence is focused on staff from healthcare and local authority settings. It is therefore unknown whether the existing approach to HCS training is appropriate and sufficient for service providers from the VCSE, who are unlikely to have a healthcare background. Therefore, the aim of this study is to assess the appropriateness of HCS training for service providers from the VCSE, by exploring and comparing their training experiences with pharmacy students (a comparison group with a healthcare background). To do this, we will conduct a mixed-methods realist evaluation to assess whether HCS equips VCSE providers to deliver MECC effectively. The findings will allow us to make recommendations about whether HCS training should be adapted depending on the setting and if so, what changes are required. The collection of both data streams will utilise existing delivery of HCS training by public health practitioner Robert Anderson-Weaver from Portsmouth City Council. Portsmouth was selected as the study location as it covers a diverse area and is the epicentre of advanced implementation of HCS training.
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 | 0.134 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".