Bibliographic record
Abstract
Older people and COVID-19 experiences and lessons for ageingThis collection is rich in experiences of less heard from groups.Stevenson and Atkinson's joint personal account of intergenerational living in England in COVID times demonstrates the value of connection and communication for two people shielding with each other during the pandemic.We learn how humour and respect led to mutual inspiration and thriving.Themes of connection and trying out technology are also explored in Atack's phenomenological study of members of a community centre in Canada that had moved activities to a virtual platform early in the COVD-19 pandemic.This resonates with Stevenson and Atkinson's account, because participants describe the pandemic experience as leading to disrupted and therefore different understandings of people they already knew very well.Clearly, people were missing physical contact and routines and were feeling unease yet they too found resilience and ways of coping.The enforced isolation brought heightened attentiveness to connections and communication.Gains and losses may relate to peoples' history of opportunities and constraints on their fund of opportunities to build social capital.Exemplifying this is Schime's account of disconnection and connection for a community of Bhutanese older adults living in New South Wales, Australia.We read in this about a group that experienced trauma before migration to Australia later in life, and for whom connection to non-co-ethnics can be problematic.Schime describes the meanings, connections and cultural well-being that can be developed through appreciating agriculture in a rural setting.At the time of writing, Australia had a low rate of infection, but stringent infection control measures that brought social isolation for many older adults.This account helps us learn about the dynamic between the experience of COVID management measures and previous experiences of stigmatisation and trauma.Also in Australia, Gates discusses the age-related social vulnerabilities in COVID-19 of lesbian, gay, bisexual, transgender and gender diverse, intersex and queerþ (LGBTIQþ) older adults.The study investigates internet use of LGBTIQþ older adults during pre-COVID and COVID times, with perhaps the unexpected finding that participants actually used the internet less in COVID times.They make a recommendation for service providers and social welfare advocates to work towards more digital inclusion for people in these groups.It is clear that the pandemic has had profound effects for many older adults' lives.But it is also clear from all of these that older adults continued to learn, re-connect and grow in some surprising ways to get through these new challenging experiences and, in some cases, to thrive.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.471 | 0.272 |
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".