Development and cognitive testing of the Nottwil Environmental Factors Inventory in Canada, Switzerland, and the USA
Bibliographic record
Abstract
OBJECTIVE: To develop and pre-test the Nottwil Environmental Factors Inventory (NEFI), a questionnaire assessing the perceived impact of environmental factors on specific areas of participation (productive life, social life, and community life) experienced by people with spinal cord injury. SUBJECTS/PATIENTS: Thirty-seven participants with spinal cord injury in Canada, Switzerland and the USA. METHODS: A first draft of the NEFI was developed based on a new theoretical model, the International Classification of Functioning, Disability and Health (ICF) Core Sets for spinal cord injury, and expert consultation. Three rounds of cognitive testing were conducted to examine participants’ comprehension of the conceptual framework and items, to identify challenges in cross-cultural measurement, and iteratively to refine the questionnaire. RESULTS: Participants were able to differentiate well between environmental factors influencing productive life and those influencing social life or community life, but not between environmental factors influencing social life and community life. Items intended to capture avoidance of participation due to barriers or overcoming of obstacles were generally well understood. CONCLUSION: For people with spinal cord injury, the NEFI may help to identify limiting and helpful environmental factors, while considering avoiding and overcoming behaviours. Quantitative validation and exploration of the possible use of the NEFI in other diagnostic groups is recommended.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".