Empowering Seniors for Quality Life (ESQ)
Bibliographic record
Abstract
Background: With a mission to create a better quality of life through health and social support amongst disadvantaged populations, the Health & Social Research Centre (HSRC) Inc. started working with seniors with a goal to empower them for a quality life. Considering the high need for social services and programming HSRC implemented a one-year project ‘Empowering Seniors for Quality Life’ in the NW region of Calgary, funded by the Federal Government of Canada. Approach: The project focused on empowering 25 seniors with digital literacy and edu-entertainment sessions. Volunteerism was the key approach. Needs and interests of the seniors were identified by conducting a need- assessment and were addressed through education and entertainment sessions on diverse topics of their interests. Results: An evaluation was conducted focusing on four thematic areas; digital literacy, sense of belonging, socialization, and health education. Seniors dove into understanding the digital world and improved their skills with the support of program volunteers. Seniors felt a sense of belonging and purpose in life where often they felt forgotten. They shared their enthusiasm for the edu-entertainment sessions where they have made new friends. Even though language was a barrier, seniors were able to communicate through actions and a new language of understanding. They enjoyed learning on diverse topics and entertainment sessions. Conclusion: There are continued high motivations from seniors to participate in programming that encourages engagement, learning and social interactions by creating a safe space. ESQ has successfully provided a platform for the seniors who often find services inaccessible.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".