RADAR-ES: A Methodological Framework for Conducting Environmental Scans in Health Services Delivery Research
Bibliographic record
Abstract
Aim: To propose a methodological framework for conceptualizing, planning, and implementing an environmental scan (ES) in health services delivery research (HSDR). Background: An ES is a methodological approach employed to examine a range of practices, policies, issues, programs, technologies, trends, and opportunities from a variety of data sources to inform program or policy development. Despite the wide use of ESs in health care to inform decision-making, a lack of methodological guidance exists to support researchers in planning and conducting an ES in HSDR. Methods: Adapting McMeekin et al’s process for developing methodological frameworks, we identified literature that described approaches to planning and conducting ESs in addition to exemplar articles that featured ESs in HSDR. We integrated original research findings and synthesized data from all sources to generate an evidence-informed methodological framework. Results: We developed RADAR-ES that consists of 5 phases and is informed by 4 guiding principles: (1) R ecognizing the Issue; (2) A ssessing Factors for ES; (3) D eveloping an ES Protocol; (4) A cquiring and Analyzing the Data; and (5) R eporting the Results. Conclusion: RADAR-ES will provide comprehensive guidance for researchers and health services stakeholders who plan and conduct ESs in HSDR.
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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 | 0.672 | 0.532 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".