User Experiences of the McMaster Optimal Aging Portal’s Evidence Summaries and Blog Posts: Usability Study
Bibliographic record
Abstract
BACKGROUND: Evidence summaries and blogs can support evidence-informed healthy aging, by presenting high-quality health research evidence in plain language for a nonprofessional (citizen) audience. OBJECTIVE: Our objective was to explore citizens' perceptions about the usability of evidence summaries and blog posts on the Web-based McMaster Optimal Aging Portal. METHODS: Twenty-two citizens (aged 50 years and older) and informal caregivers participated in a qualitative study using a think-aloud method and semistructured interviews. Eleven interviews were conducted in person, 7 over the telephone, and 4 by Skype. RESULTS: We identified themes that fell under 4 user-experience categories: (1) desirability: personal relevance, (2) understandability: language comprehension, grasping the message, dealing with uncertainty, (3) usability: volume of information, use of numbers, and (4) usefulness: intention to use, facility for sharing. CONCLUSIONS: Participants recognized that high-quality evidence on aging was valuable. Their intended use of the information was influenced by how much it applied to their own health circumstances or those of a loved one. Some specific formatting features that were preferred included consistent layout, content organized by subheadings, catchy titles, numerical information summarized in a table, and inclusion of a glossary.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".