Editorial: Learning for action in policy implementation
Bibliographic record
Abstract
integration of legal epidemiology and IS (Lane and Stergachis); a measurement study on acceptability and feasibility of policy implementation strategies (Purtle et al.); one qualitative comparative case study on policy intermediaries (Bullock et al.); and one case study on food assistance policy implementation (Kenney et al.); one quantitative evaluation effective communication related to policy IS (Dodson et al.); one mixed-method study on inter-sector care for the homeless (Martins et al.); one design study on participatory development of a target policy profile (Means et al.). Reviewing the papers included in this Research Topic, we identified several themes salient to the future of policy IS.Policies include regulatory, promotional, and redistributive decisions and guidelines for implementing programs to achieve societal goals. 1 Lane and Stergachis addressed the importance of systematic collection and coding of laws to enable policy implementation analysis. It is notable to observe that a consensus was implicitly reached in the 12 manuscripts regarding the importance of the evidence base for policy and practice (EBPP). The working definition of policy IS in the 12 manuscripts aligns with the National Cancer Institute definition of policy IS. 2 However, from a policy perspective, research evidence is not the only input into policy decisions and that is why evidence-informed policies and practices (EIPP) was raised as a critical concept. 3 In public administration, policy implementation is defined as a deliberate, sanctioned change to public policy legitimized by a political authority, with an emphasis on changing the status quo and adapting to diverse contexts. 4 Scientific evidence is considered as one of multiple resources in implementation. Policy implementation strategies include information campaigns (Kenney et al.), licensing (Bera et al.), as well as others (e.g., contracting, subsidies, accreditation).Through literature review and synthesis, Tao et al. highlighted policy implementation strategies, including training, resource reallocation, and increased insurance coverage.Policy IS has distinctive challenges in different settings and contexts (e.g., global vs. domestic). Incorporating contextual contingencies is important to address the factors affecting policy IS. List et al. brought the concept of Global North (United States, Canada, and Western Europe) and Global South in the discussion of global policy implementation in decolonizing global health. They highlighted donors as the bridge and power to connect two types of partners and reflected on who produced the frameworks and whether it was reciprocal.In synthesizing scientific evidence, conceptual frameworks are crucial to cluster the findings and aggregate knowledge, with the potential of theorization. Health Triangular Policy Framework by O'Brien et al. (2020) was applied in the narrative review by Tao et al., with an emphasis on actor-relevant contexts, contents, and processes. 5 In survey-based studies with policymakers and implementers, low response rates are a general challenge. For example, Dodson et al. had a 4.5% response rate in a national survey of local officials. Nonetheless, the results from these studies shed light on policy implementation practice. Dodson et al. studied the strategies to deliver policy briefs to facilitate information dissemination to local policymakers. Their study found that the narrative policy briefs had the lowest score (42%) related to strength in reasoning. In contrast, usual-care and risk-framing brief types had significantly higher scores to reflect strong reasoning (59% and 52%, respectively). This Research Topic collected diverse studies to learn for action. Bullock Challenges in policy IS include the complexity of developing overarching theories that address diverse contexts and evolving implementation partners. Contextual analysis often lacks direct causal links to outcomes, while randomized trials are difficult due to the nature and scale of policy implementation. Other useful policy IS methods include legal epidemiology (Lane and Stergachis) and coincidence analysis. 7 Additionally, measuring outcomes across macro, meso, and micro levels, accounting for both intended and unintended effects, remains complex.A limitation of this Research Topic is that all of the manuscripts are in health and related domains such as food and nutrition (Kenney et al. and Smith et al.) and housing instability (Martins et al.). We anticipate that future Research Topics will cover other social policies, such as unemployment, poverty, education, and LGBTQIA+ marriage. We also anticipate future studies on systematic reviews to understand the overarching landscape of policy implementation, modeling studies to predict policy impacts, costing methods for use in policy implementation, and others.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".