Exploring Identity and Illness Narratives: Studying Young Womenâs Experiences of Cystic Fibrosis
Bibliographic record
Abstract
Medical advancements and research initiatives in the last two decades have changed the experience of growing up with a chronic illness. Young people living with Cystic Fibrosis (CF), a chronic, life-threatening, life-limiting, genetic disease, have benefitted from these advances and are living fuller, healthier, longer lives than previously thought possible. Literature exploring the experiences of young people living with CF has traditionally relied on information from caregivers and health care practitioners. It does not reflect the diverse experiences of young people today, or explore the subjective meanings constructed from experiences. Using a social constructionist and narrative inspired methodology, this study explores illness narratives and identity constructions among three young women living with CF. Their narratives are broad and diverse. Shared elements include; making meaning of their illness, and constructing a multi-faceted, relational, layered and flexible sense of self. The layered experiences of CF are one of many important factors influencing their unfolding identity. Relational processes and socially constructed norms and expectations of illness, health, and gender also influence participants’ unfolding sense of self. This study demonstrates the value of rich conversations exploring identity construction and illness narratives, and the complexities and nuances within individual experiences.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".