Use evidence to expose the unequal distribution of problems and the unequal distribution of solutions
Bibliographic record
Abstract
Mark Petticrew argues that we need to increase the size and strength of the evidence base on policies and programmes to reduce inequalities in health.1 He cautions us not to be drawn unnecessarily into (ill informed or tangential) debates about methods. He suggests that while our research is proceeding, much can be done with what evidence we have. He is right on all three fronts. Primary research is sorely needed to establish the effectiveness and economic efficiency of many interventions that could make a major impact in health inequalities. The current evidence imbalance is an embarrassment. A vet treating a puppy for an ear infection can draw on level 1 evidence on the options available,2 while policy makers responsible for reducing inequalities in human populations are forced to work with far less certainty.3 But what to do with what we have got? More than 20 years ago Murrell argued that endless tabulations of data about the distributions of social problems are among the least likely of all research processes to alter the status quo regarding what is done to address them.4 Yet that is what most of us find easiest to do in population health: our report cards on our cities, regions or countries documenting the finer points of how health inequalities are getting worse. One pathway is the type of data-for-action impetus that comes from the intense, in-it-for-the-long-haul university-community research partnerships that colleagues in some of the poorest communities have established.5 This requires a special type of research6 and, we would argue, a special type of researcher. In addition, we could simply reorient the day jobs of regular population health data analysts. Imagine how powerful it would be if, alongside maps of the areal distribution of smoking rates, obesity, premature mortality or per capita prescriptions for depression, our analysts were mandated to report on the distribution of polices and practices known to be effective in their prevention? Like smoke free public places, fair wage policies, affordable fresh food, confectionery-free schools, family friendly workplaces and so on? Imagine how much easier it would be for the public to claim their entitlement to prevention if they could see how unequally (and potentially unfairly) the policies and practices are currently distributed. ‘Geography is destiny’ is a phrase well known in some circles. If this were made more public it would be political dynamite, not for the factors the public tend to think they cannot change (who they are and where they live), but because such maps would expose the inertia of those responsible for allocating the resources that can alter those destinies. In the field of tobacco control, Glantz and Balbach tell us that research evidence documenting the harmful effects of tobacco and the effectiveness of measures to prevent its use provided an essential backdrop. But it was never going to be enough to change policy.7 What was critical was the creation of a constituency for change mobilized in part by creative re-framing of the issues being addressed (from individual choice to the public good) and of the solutions being offered. This strengthened decision maker resolve and helped overcome opposition. It was not the quality of the evidence that counted so much as its weight and the way this was brought to bear on the problem. Let's learn from this. Population health data observatories have significant expertize and experience in mapping health behaviours, health outcomes and health inequalities [see for example the annual reports of the Association of Public Health Observatories in England (www.AHPO.org.uk)]. Inventories of relevant policies in some sectors are already being collected systematically [see Journal of School Health 2001; 71(1) Supplement, which describes the CDC School Health Policy and Programs study]. The next step is to bring these two together. In the field of health inequalities, our capacity to make a difference may be closer than we think. P.Hawe and A.Shiell are Health Scientists funded by the Alberta Heritage Foundation for Medical Research. P.Hawe holds the Markin Chair in Health and Society. The Population Health Intervention Research Centre is a centre for research development in population health funded by the Canadian Institutes of Health Research.
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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 | 0.061 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".