A CITIZEN SCIENCE PROJECT ENGAGING NIGERIAN OLDER PERSONS IN A NEIGHBORHOOD ASSESSMENT FOR PHYSICAL ACTIVITY
Bibliographic record
Abstract
Abstract A main tenet of age-friendly principles is that the voices of older people should be guiding improvements to their communities. The purpose of this collaborative citizen science project was to examine the age-friendliness of a neighborhood in Lagos Nigeria in terms of its characteristics to enable physical activity. Citizen scientists (13 older adults, seven men and six women, 65 to 86 years old) were involved in data collection, analysis, and priority setting. For data collection, they used the Stanford Healthy Neighborhood Discovery Tool application on a tablet to record 156 photos and 151 commentaries (text and audio) of neighborhood barriers and facilitators to physical activity. The collaborative analysis and priority setting process led to the following facilitators being identified: pedestrian and traffic facilities (e.g., traffic lights, walkways); green areas and parks; multi-generational community features (e.g., programs / facilities); opportunities for social connection (e.g., neighborhood associations, churches); safety of destinations and services; and public toilets. Barriers included: hazardous walkways / traffic; noise pollution; refuse; selling of public parks; crime (e.g., kidnapping, criminal hideouts); no safe drinking water; and ageism. The identified priorities for change in this Nigerian neighborhood included both social and physical aspects of the environment: social connectivity; improved pedestrian and traffic facilities; and green and beautiful environments. By meaningfully engaging older adults, this type of approach shows promise for communities in making age-friendly changes.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".