Social return on investment (SROI) method to evaluate physical activity and sport interventions: a systematic review
Bibliographic record
Abstract
BACKGROUND: Physical Activity and Sport (PAS) interventions can reduce the social and economic burden of non-communicable diseases and improve the wellbeing of the population. Social return on investment (SROI) has the capacity to measure broader socio-economic outcomes in a singular monetary ratio to help identify the most impactful and cost-beneficial intervention. This review aimed to systematically identify and review studies using the SROI method within the field of PAS and assess their quality. METHODS: Peer-reviewed and grey literature SROI studies were identified through a systematic search of six databases. Two reviewers independently assessed the identified studies to determine eligibility. Study quality was assessed using the Krelv et al. 12-point framework. For each included study, information was extracted and classified into summary tables. Extracted information included study and participant characteristics, type of outcomes and SROI ratio. The PRISMA guidelines were followed. RESULTS: Seventeen studies published between 2010 and 2018 met the inclusion criteria. Most studies (94%) were non-peer reviewed publicly available reports, primarily conducted in the UK (76%), by private consulting firms (41%) and included all types of stakeholders (76%). PAS interventions included Primary prevention (47%), Sport for development (29%), Secondary and tertiary prevention (18%) and High-performance sport (6%). SROI ratios, which report the social value created in relation to the cost of an intervention, vary between 3:1 and 124:1 for the high-quality studies. CONCLUSIONS: The SROI framework can be a useful tool to inform policy-making relating to PAS investment as it can account for the wide societal benefits of PAS. The quality of studies in the field would benefit from the employment of an impact map (or logic model), reporting negative outcomes and using objective study designs. The application of the SROI method in the PAS field is relatively recent, and thus further research would be beneficial to promote its potential for policy-making bodies in the field.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".