Improving health equity through theory-informed evaluations: A look at housing first strategies, cross-sectoral health programs, and prostitution policy
Bibliographic record
Abstract
The emergent realist perspective on evaluation is instructive in the quest to use theory-informed evaluations to reduce health inequities. This perspective suggests that in addition to knowing whether a program works, it is imperative to know ‘what works for whom in what circumstances and in what respects, and how?’ (Pawson & Tilley, 1997). This addresses the important issue of heterogeneity of effect, in other words, that programs have different effects for different people, potentially even exacerbating inequities and worsening the situation of marginalized groups. But in addition, the realist perspective implies that a program may not only have a greater or lesser effect, but even for the same effect, it may work by way of a different mechanism, about which we must theorize, for different groups. For this reason, theory, and theory-based evaluations are critical to health equity. We present here three examples of evaluations with a focus on program theories and their links to inequalities. All three examples illustrate the importance of theory-based evaluations in reducing health inequities. We offer these examples from a wide variety of settings to illustrate that the problem of which we write is not an exception to usual practice. The ‘Housing First’ model of supportive housing for people with severe mental illness is based on a theory of the role of housing in living with mental illness that has a number of elements that directly contradict the theory underlying the dominant model. Multisectoral action theories form the basis for the second example on Venezuela's revolutionary national Barrio Adentro health improvement program. Finally, decriminalization of prostitution and related health and safety policies in New Zealand illustrate how evaluations can play an important role in both refining the theory and contributing to improved policy interventions to address inequalities. The theoretically driven and transformative nature of these interventions create special demands for the use of theory in evaluations.
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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 | 0.068 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".