Trading quality for relevance: non-health decision-makers’ use of evidence on the social determinants of health
Bibliographic record
Abstract
OBJECTIVES: Local government services and policies affect health determinants across many sectors such as planning, transportation, housing and leisure. Researchers and policymakers have argued that decisions affecting wider determinants of health, well-being and inequalities should be informed by evidence. This study explores how information and evidence are defined, assessed and utilised by local professionals situated beyond the health sector, but whose decisions potentially affect health: in this case, practitioners working in design, planning and maintenance of the built environment. DESIGN: A qualitative study using three focus groups. A thematic analysis was undertaken. SETTING: The focus groups were held in UK localities and involved local practitioners working in two UK regions, as well as in Brazil, USA and Canada. PARTICIPANTS: UK and international practitioners working in the design and management of the built environment at a local government level. RESULTS: Participants described a range of data and information that constitutes evidence, of which academic research is only one part. Built environment decision-makers value empirical evidence, but also emphasise the legitimacy and relevance of less empirical ways of thinking through narratives that associate their work to art and philosophy. Participants prioritised evidence on the acceptability, deliverability and sustainability of interventions over evidence of longer term outcomes (including many health outcomes). Participants generally privileged local information, including personal experiences and local data, but were less willing to accept evidence from contexts perceived to be different from their own. CONCLUSIONS: Local-level built environment practitioners utilise evidence to make decisions, but their view of 'best evidence' appears to prioritise local relevance over academic rigour. Academics can facilitate evidence-informed local decisions affecting social determinants of health by working with relevant practitioners to improve the quality of local data and evaluations, and by advancing approaches to improve the external validity of academic research.
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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 | 0.466 | 0.760 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.045 | 0.051 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".