Can Pictograms Help Older Adults to Assess Their Own Level of Functional Capacity? Using Human Centered Design Methods to Develop and Validate Pictograms for the Functional Assessment of Activities of Daily Living
Bibliographic record
Abstract
Despite the potential value of image-based tools, and the fact that self-reported functional status has been shown to be reliable and have predictive validity, few visual-based assessment tools, or mixed-modality tools incorporating pictograms, for function exist. The main objectives were to 1) Explore the potential benefit of visual based tools (i.e., pictograms) for the use of self-assessment tools; 2) Present methods for co-designing clinical pictograms with older adults; and, 3) Discuss the prospect of using pictograms for self-assessment tools and discuss the prospect of using pictograms for self-assessment tools. From an accessibility perspective, the use of visuals may be particularly beneficial in individuals, like older adults, that have accessibility needs, such as cognitive impairment. The development of pictograms to measure a concept requires careful consideration to ensure the graphical representational aligns with the intended meaning, which is context dependent. The development of pictograms for self-administered functional assessments tools may increase the accessibility and ease of use of self-assessment tools for older adults, given the limitations of current text-based tools.
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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 | 0.001 | 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 | 0.000 |
| 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".