Living the aging in Senegal Perceptions/representations and coping strategies of persons of age three: results of investigations retrospective
Bibliographic record
Abstract
In Senegal, due to the young age of the population (60%) the concerns of the old tend to be put in the background. And yet, problems related to old age are a reality. These problems come up not in terms of demography (the old represent only 4.7% of the population), but in terms of the breaking-up of the social fabric, urbanization and the dismantling of the solidarity and poverty networks. This work is based on a collection of qualitative data from three studies conducted between 2008 and 2011 with the elderly to assess their real- life experiences, their perception of aging, their challenges and coping strategies. The results showed a transformation in the role and status of the old; this transformation being caused by social and society-related mutations. As a result, the inter-generation solidarity links have much loosened in the urban areas making the old people more vulnerable (in economic, social, health terms), especially those in charge of a family. The situation has been made worse by the unemployment affecting their offspring. In addition, the old people, who are often suffering from chronic diseases, find it hard to take charge of their medical expenses, despite the institution of the National Sesame Health Plan for the old or free health care policy. This has made them even more vulnerable. Yet before this precarious situation, the elderly develop strategies to cope with difficulties. Some recommendations have been made with a view to improving their lives and socioeconomic condition.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".