Measuring impact of environmental factors on human functioning and disability: a review of various scientific approaches
Bibliographic record
Abstract
PURPOSE: The objective of this paper is to present a framework for systematically describing different approaches to measure environmental factors (EF) and to discuss some strengths and weaknesses of these approaches. METHODS: Identification of suitable criteria for ordering measurements of EF was based on an analysis of existing reviews, a qualitative literature review and feedback from experts. Items of selected EF measures were linked to the International Classification of Functioning, Disability and Health. RESULTS: Experimental and observational designs for the study of EF are distinguished and illustrated with examples. Approaches to study EF are differentiated into those directly measuring an environmental interaction with function and those relying on an independent assessment of environmental features. By applying these criteria, a three-dimensional matrix framework for measurement of EF in observational studies is developed. CONCLUSION: The acknowledgement of different measurement approaches to the scientific study of EF is one pathway towards an increased understanding of the connection between environments and functioning. Many different measures may be used to approximate the realities of disabling or enabling environments. This review provides an initial framework for improving our fundamental comprehension of the complexity of the measurement of EF in the context of human health and disability.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".