Conducting Comprehensive Environmental Scans in Health Research: A Process for Assessing the Subject Matter Landscape
Bibliographic record
Abstract
Environmental scans provide researchers with an assessment of the landscape around an issue of interest. In this process relevant information is systematically amassed to identify current status, scopes or opportunities, and risks. This paper aims to serve as a basic and surface level guide to understanding and planning for conducting an environmental scan. The intended audience includes students and researchers new to the use of environmental scans. Before discussion of all the steps, some examples of the use of environmental scans in health research is provided. The process of conducting an environmental scan is outlined in five steps that revolve around purpose, people, questions, information gathering and presenting. The paper concludes with a discussion on advantages and challenges of conducting environmental scans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.305 | 0.284 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".