‘You learn to live with all the things that are wrong with you’: gender and the experience of multiple chronic conditions in later life
Bibliographic record
Abstract
This article examines how older adults experience the physical and social realities of having multiple chronic conditions in later life. Drawing on data from in-depth interviews with 16 men and 19 women aged 73+ who had between three and 14 chronic conditions, we address the following research questions: (a) What is it like to have multiple chronic conditions in later life? (b) How do older men and women 'learn to live' with the physical and social realities of multiple morbidities? (c) How are older adults' experiences of illness influenced by age and gender norms? Our participants experienced their physical symptoms and the concomitant limitations to their activities to be a source of personal disruption. However, they normalised their illnesses and made social comparisons in order to achieve a sense of biographical flow in distinctly gendered ways. Forthright in their frustration over their loss of autonomy and physicality but resigned and stoic, the men's stories reflected masculine norms of control, invulnerability, physical prowess, self-reliance and toughness. The women were dismayed by their bodies' altered appearances and concerned about how their illnesses might affect their significant others, thereby responding to feminine norms of selflessness, sensitivity to others and nurturance. We discuss the findings in relation to the competing concepts of biographical disruption and biographical flow, as well as successful ageing discourses.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".