Re-integrating Older Adults Who Have Recovered from the Novel Coronavirus into Society in the Context of Stigmatization: Lessons for Health and Social Actors in Ghana
Bibliographic record
Abstract
The novel coronavirus (COVID-19) was first identified in Wuhan, China in December 2019 and has become one of the most serious public health crisis in the world. Pronounced stigmatization among COVID-19 patients, including those who have recovered stems from three overlapping factors. First, the disease is new and for that matter there are still many unknowns. Second, people are often afraid of the unknown. Third, it is easy to associate that fear with other people. Public health stakeholders and social workers should make an effort to integrate anti-stigma measures into public health and social work education to prevent stigmatization. Public health actors and social workers should organize frequent meetings, fora, workshops, and conferences on radios and televisions for sensitization of community members on the consequences of stigmatization. Besides, as a way of ensuring that the rights of older adults who have recovered from the COVID-19 infection are protected, efforts must be made to shield their identities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".