Bibliographic record
Abstract
The number of people with cystic fibrosis (CF) graduating from high school and pediatric care has rapidly increased. With improved survival, fulfilling meaningful lives through education, careers, relationships and having a family are now opportunities most people with CF can contemplate and drive toward. Planning for the future is a key component for care providers to broach at all stages of care delivery for individuals with CF from the newborn to the older adult. This chapter explores key areas of the lives of adolescents and adults with CF and includes career choices, independent living, health insurance, social security and disclosing a CF diagnosis. While it is recognized that health insurance and social safety nets vary by country, we have used the systems in Australia, Canada, United Kingdom and United States to demonstrate similarities and differences for care coverage. The work also discusses the impacts of choosing a career in healthcare for a person with CF and the likely impacts of enhanced quality of life with the prescription of CF transmembrane conductance regulator modulators to a growing proportion of the CF population. Lessons of virtual care delivery especially during the pandemic are also discussed, as well as how these lessons may support the adult with CF to pursue education, careers and to live their lives to the fullest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.032 |
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".