If You Build It, Who Will Come? A Description of User Characteristics and Experiences With the McMaster Optimal Aging Portal
Bibliographic record
Abstract
Objectives: The McMaster Optimal Aging Portal (the Portal) aims to increase access to evidence-based health information. We would now like to understand who uses the Portal, why, and for what, and elicit feedback and suggestions for future initiatives. Methods: An online survey of users collected data on demographics, eHealth literacy, Internet use, information-seeking behavior, site acceptability and perceived impact on health behaviors, participant satisfaction, and suggestions for improvements using mixed methods. Results: Participants ( n = 163, age 69.8 ± 8.6 years) were predominantly female (76%), married (67%), retired (80%), and well-educated with very good/excellent health (55%). The Portal was easy to use (83%) and relevant (80%), with 68% intending to, and 48% having changed behavior after using the Portal. A number of suggestions for improvement were obtained. Discussion: A better understanding of users’ characteristics, needs, and preferences will allow us to improve content, target groups who are not engaging with the Portal, and plan future directions.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".