Knowledge translation strategies for sharing evidence-based health information with older adults and their caregivers: findings from a persona-scenario method
Bibliographic record
Abstract
BACKGROUND: By understanding the information seeking behaviors of older adults, we can better develop or iterate effective information technologies, such as the McMaster Optimal Aging Portal, that provide evidence-based health information to the public. This paper reports health-related information seeking and searching behaviours and provides strategies for effective knowledge translation (KT) to increase awareness and use of reliable health information. METHODS: We conducted a qualitative study with eighteen older adults using the persona-scenario method, whereby participants created personas and scenarios describing older adults seeking health information. Scenarios were analyzed using a two-phase inductive qualitative approach, with the personas as context. From the findings related to pathways of engaging with health information, we identified targeted KT strategies to raise awareness and uptake of evidence-based information resources. RESULTS: Twelve women and six men, 60 to 81 years of age, participated. In pairs, they created twelve personas that captured rural and urban, male and female, and immigrant perspectives. Some scenarios described older adults who did not engage directly with technology, but rather accessed information indirectly through other sources or preferred nondigital modes of delivery. Two major themes regarding KT considerations were identified: connecting to information via other people and personal venues (people included healthcare professionals, librarians, and personal networks; personal venues included clinics, libraries, pharmacies, and community gatherings); and health information delivery formats, (e.g., printed and multimedia formats for web-based resources). For each theme, and any identified subthemes, corresponding sets of suggested KT strategies are presented. CONCLUSIONS: Our findings underline the importance of people, venues, and formats in the actions of older adults seeking trusted health information and highlight the need for enhanced KT strategies to share information across personal and professional networks of older adults. KT strategies that could be employed by organizations or communities sharing evidence-based, reliable health information include combinations of educational outreach and materials, decision support tools, small group sessions, publicity campaigns, champions/opinion leaders, and conferences.
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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 | 0.030 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".