CO-CREATING EDUCATIONAL MATERIALS WITH OLDER ADULTS AND INFLUENCING HEALTH CARE DECISION-MAKERS
Bibliographic record
Abstract
Abstract Rural and small city older adults’ views on primary and community care restructuring indicated a strong desire to provide input directly to healthcare decision-makers through oral over written means and collective rather than individual dialogues (Hulko et al., 2020). Thus, through integrated knowledge translation (iKT), we worked with older adults to (a) interpret our research findings on primary and community care restructuring; (b) create KT tools (grassroots infographic, key messages, documentary); and, (c) host a knowledge summit where we released our KT tools and facilitated discussions between service users and administrators (decision-makers, knowledge users, seniors advocates) about ways to move our findings into practice and affect service delivery. 30 people attended the knowledge summit and were invited to provide feedback on the iKT process and KT tools; 23 (8 service users, 15 administrators) filled out the feedback questionnaires. Service users provided positive feedback about the opportunity to partner with service providers, particularly senior administrators. Administrators recognized the need to shift from being “system focused” to “patient focused,” which can be facilitated through engagement of rural older adults in service delivery decisions. They recognized that service users had “good ideas on how to deliver services locally” and appreciated the opportunity to hear service users’ perspectives and needs, communicate face-to-face, and “discuss possible solutions” together. Our work highlights how emancipatory pedagogy - actioning research findings in collaboration with older adults - can enable mutually beneficial exchanges of ideas and solutions between service users and administrators and lead to improved health service delivery.
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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 | 0.046 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".