Geographies of wellbeing and chronic illness: A Canadian case study
Bibliographic record
Abstract
Abstract Geography has had a long legacy of research investigating the lived experiences of individuals with chronic illness, particularly women. Health geographers have also begun to focus beyond health to wellbeing, defined as that space between expectation and reality. Individuals living with chronic illness often experience complex impacts to their lifestyle, relationships, aspirations, and employment that affect this space, expressed as emotional, mental, and psychosocial health and wellbeing impacts. Systemic lupus erythematosus (SLE) is a chronic autoimmune condition disproportionately affecting women. Lupus nephritis (LN) is a manifestation of SLE that affects the kidneys, developing in up to 60% of those with SLE. Impacts extend beyond the physiological, as LN affects capacity to conceive and carry a child. Individuals aged ≥18 years with LN were recruited from a Canadian lupus cohort to participate in semi‐structured in‐depth interviews (n = 30). At the individual level, participants reported emotional impacts experienced at LN diagnosis (e.g., uncertainty, fear, surprise, relief) and in daily life (e.g., fear of future, stigmatization). At the meso level, participants shared how relationships (e.g., feeling supported, treated differently), leisure activities (e.g., modifying activities), paid employment and education (e.g., altered career paths), and family planning (e.g., conception challenges) impacted wellbeing. Results increase our understanding of how living with chronic illness impacts wellbeing and can inform policy and practice change.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.035 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".