Guidelines for Creating Senior-Friendly Product Instructions
Bibliographic record
Abstract
Although older adults feel generally positive about technologies, many face difficulties when using them and need support during the process. One common form of support is the product instructions that come with devices. Unfortunately, when using them, older adults often feel confused, overwhelmed, or frustrated. In this work, we sought to address the issues that affect older adults’ ability to successfully complete tasks using product instructions. By observing how older adults used the product instructions of various devices and how they made modifications to simplify the use of the instructions, we identified 11 guidelines for creating senior-friendly product instructions. We validated the usability and effectiveness of the guidelines by evaluating how older adults used instruction manuals that were modified to adhere to these guidelines against the originals and those that were modified by interaction design researchers. Results show that, overall, participants had the highest task success rate and lowest task completion time when using guideline-modified user instructions. Participants also perceived these instructions to be the most helpful, the easiest to follow, the most complete, and the most concise among the three. We also compared the guidelines derived from this research to existing documentation guidelines and discussed potential challenges of applying them.
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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 | 0.031 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".