Content analysis of the Measure of the Quality of the Environment by linkage with the International Classification of Functioning, Disability and Health
Bibliographic record
Abstract
BACKGROUND: This study explores the linkage between the Measurement of Environmental Quality (MQE) and the International Classification of Functioning, Disability, and Health (ICF). Stemming from the Human Development Model-Disability Creation Process (HDM-DCP), MQE enhances understanding of how environmental quality impacts disability development across diverse socio-cultural contexts. Integrating MQE with ICF expands the perspective on disability formation beyond HDM-DCP, encompassing ICF's functioning approach. OBJECTIVE: To link the MQE with the concepts and categories of the ICF. METHODS: Two health professionals with adequate taxonomic knowledge of the ICF performed the initial linkage, which was based on updated standardized rules considering all hierarchical levels of the ICF. Linkage agreement between the first two assessors was measured using the Kappa (k) coefficient and respective 95% confidence intervals. In the absence of a consensus between the two assessors (k > 0.60), a third assessor was consulted to make the arbitrary decision of the final categories linked to the MQE. RESULTS: Insufficient agreement between the two assessors was found for the linkage process (k = 0.52; p < 0.001), requiring the final decision from the third assessor. At the end of the process, 26 ICF categories were linked to the main concepts (MC) measured by the 26 items of the short version of the MQE. Ten ICF categories were linked to the additional concepts (AC) measured by the MQE. Moreover, the MQE addresses the five domains of the ICF component "environmental factors," with a predominance of the "services, systems and policies" domain (MC = 45.8% and AC = 40%). CONCLUSION: The linkage of the concepts measured by the MQE to ICF categories enabled mapping the content of the MQE, identifying it as a promising tool for measuring environmental factors in accordance with ICF percepts.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".