Osteoporosis knowledge translation for young adults: new directions for prevention programs
Bibliographic record
Abstract
INTRODUCTION: Osteoporosis prevention is heavily reliant on education programs, which are most effective when tailored to their intended audience. Most osteoporosis prevention education is designed for older adults, making application of these programs to younger adults difficult. Designing programs for young adults requires understanding the information-seeking practices of young adults, so that knowledge about osteoporosis can be effectively translated. METHODS: Individual interviews were conducted with 60 men and women-multiethnic, Canadian young adults-to explore both the sources and types of information they search for when seeking information on nutrition or bone health. RESULTS: The results of this study raised themes related to the sources participants use, to their interests and to ways of engaging young adults. Prevention programs should make use of traditional sources, such as peers, family members and medical professionals, as well as emerging technologies, such as social media. Choice of sources was related to the perceived authority of and trust associated with the source. Messaging should relate to young adult interests, such as fitness and food-topics on which young adults are already seeking information-rather than being embedded within specific osteoporosis awareness materials. Engaging young adults means using relatable messages that are short and encourage small changes. Small gender-based differences were found in the information-seeking interests of participants. Differences related to age were not examined. CONCLUSION: Creating short, action-oriented messages that are designed to encourage small changes in behaviour and are packaged with information that young adults are actively seeking is more likely to result in active engagement in prevention behaviours.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 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".