A nursing home staff tool for the indoor visual environment: The content validity
Bibliographic record
Abstract
In the Netherlands, over 40% of nursing home residents are estimated to have visual impairments. This results in the loss of basic visual abilities. The nursing home environment fits more or less to residents’ activities and social participation. This is referred to as environmental fit. To raise professional awareness of environmental fit, an Environmental Observation tool for the Visually Impaired was developed. This tool targets aspects of the nursing home environment such as ‘light’, the use of ‘colours and contrasts’ and ‘furnishing and obstacles’. Objective of this study is to validate the content of the observation tool to have a tool applicable for practice. Based on the content validity approach, we invited a total of eight experts, six eye care professionals and two building engineering researchers, to judge the relevance of the items. The Item Content Validity approach was applied to determine items to retain and reject. The content validity approach led to a decrease in the number of items from 63 to 52. The definitive tool of 52 items contains 21 for Corridors , 17 for the Common Room , and 14 for the Bathroom . All items of the definite tool received an Item-Content Validity Index of ≥ 0.88 and a Scale-Content Validity Index of ≥ 0.71. The content validity index of the scale and per item has been applied, resulting in a tool that can be applied in nursing homes. The tool might be a starting point of a discussion among professional caregivers on environmental interventions for visually impaired older adults in nursing homes.
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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 | 0.026 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".