International consensus statement on the design, delivery and evaluation of sport-based interventions aimed at promoting social, psychological and physical well-being in prison
Bibliographic record
Abstract
OBJECTIVE: To develop an international consensus statement to advise on designing, delivering and evaluating sport-based interventions (SBIs) aimed at promoting social, psychological and physical well-being in prison. DESIGN: Modified Delphi using two rounds of survey questionnaires and two consensus workshops. PARTICIPANTS: A multidisciplinary panel of more than 40 experts from 15 international jurisdictions was formed, including representation from the following groups and stakeholders: professionals working in the justice system; officials from sport federations and organisations; academics with research experience of prisons, secure forensic mental health settings and SBIs; and policy-makers in criminal justice and sport. RESULTS: A core research team and advisory board developed the initial rationale, statement and survey. This survey produced qualitative data which was analysed thematically. The findings were presented at an in-person workshop. Panellists discussed the findings, and, using a modified nominal group technique, reached a consensus on objectives to be included in a revised statement. The core research team and advisory board revised the statement and recirculated it with a second survey. Findings from the second survey were discussed at a second, virtual, workshop. The core research team and advisory board further revised the consensus statement and recirculated it asking panellists for further comments. This iterative process resulted in seven final statement items; all participants have confirmed that they agreed with the content, objectives and recommendations of the final statement. CONCLUSIONS: The statement can be used to assist those that design, deliver and evaluate SBIs by providing guidance on: (1) minimum levels of competence for those designing and delivering SBIs; (2) the design and delivery of inclusive programmes prioritising disadvantaged groups; and (3) evaluation measures which are carefully calibrated both to capture proposed programme outcomes and to advance an understanding of the systems, processes and experiences of sport engagement in prison.
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 | 0.320 | 0.316 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.012 | 0.014 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".