Doing what’s necessary to enable partners to create the conditions for physical activity in their place.
Bibliographic record
Abstract
Background: Together an Active Future (TaAF) is a place-based collective of change leaders from across organisations (local government, health, education, voluntary, faith and leisure). Its uniqueness lies in its non-hierarchical, cross-organisational and shared role model structure. The initiative has been implemented across 6 districts in the northwest of England. TaAF aims to establish locally driven cross-policy considerations for tackling physical activity inequalities in a place that has a likelihood of sustainable impact. Methods: To identify what works (enablers), for whom and in which circumstances, when creating the conditions necessary to address physical activity inequalities. Using a realist methodology, we used a combined approach of embedded process evaluation and multiple researcher-led methods. Qualitative data collection methods were employed which included 65 Stories of Significant Change, 30 Ripple Effect Mapping sessions, 62 Semi-Structured Interviews and 6 Focus Groups. The data was analysed using hermeneutic-interpretive text analysis. Participants: Over 100 participants took part in the study to explore the experiences of key stakeholders who are connected to the TaAF collaboration in a professional or participation capacity. Participants include employees, senior leaders and patients from a diverse range of systems and organisations in Lancashire. Findings: A total of 11 themes emerged from the data, which identified individual and systems enablers. The findings highlight the journey of individual behaviours through the system that can lead to favourable conditions for tackling physical activity inequalities. Individual Behaviours. The behaviours of an individual can contribute to systems/ organisational behaviour change. Participants highlighted these behaviours can easily slip and lead to siloed and disconnected working. System Enablers. Building from individual behaviours (when working relationally), these system behaviours lead to a greater likelihood of creating conditions for physical activity in a place. This also increases the likelihood of sustained and embedded systems behaviours or culture change, and in turn, increases the sustainability of the individual behaviours. Conditions for Change. These are features or ‘conditions’ that may be necessary for place-based approaches to address inequalities (Shearn et al., 2022). These conditions represent the national current understanding, based on existing theory and emerging evidence from similar initiatives in England; however, they continue to be refined as our understanding of systemic transformation grows.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".