Seeing the Invisible Resiliency (STIR): Chronic Autoimmune Conditions and Post‐Secondary Education Experiences in Young Adulthood
Bibliographic record
Abstract
INTRODUCTION: Young adults with chronic autoimmune conditions face unique and often overlooked challenges in post-secondary education due to the invisible and unpredictable nature of these conditions. This patient-led qualitative study aims to further understand the experiences of young adults living with chronic autoimmune conditions while attending or considering attending post-secondary education. METHODS: The study followed the three-phase Patient and Community Engagement Research (PaCER) approach, a participatory framework that trains individuals with lived experience to lead all stages of research. In the first stage (SET), the protocol was co-designed with three external patient-partners. Study participants included young adults (18-35 years) with a chronic autoimmune condition for > 1 year who considered attending or attended a Canadian post-secondary school within the last 5 years and were recruited through social media. Data were collected (COLLECT) via focus group and interviews and then analysed using thematic and narrative analysis. Findings were shared back with study participants (REFLECT) for refinement and to inform recommendations. RESULTS: Ten young adults participated, and eight key themes were identified. Themes included the wide-ranging impacts of disease management, the value of peer and family support, protective and risk factors for success, limited awareness and education around chronic conditions, and sometimes-unconscious burden of navigating invisible conditions. Participants also reflected on their resilience and the shifting accessibility landscape during Covid-19, and offered detailed feedback on current gaps and needed support. Their recommendations underscored ongoing institutional shortcomings and the need for systemic change. CONCLUSION: Our findings indicate that young adults living with chronic autoimmune conditions are not having their needs sufficiently met while navigating the post-secondary education system. It is imperative that changes and feedback provided by students with lived experience are implemented to ensure an accessible post-secondary education experience. PATIENT OR PUBLIC CONTRIBUTION: Seven PaCER researchers, who identify as young adults with lived experience of chronic conditions, led the study design, data collection, analysis and manuscript preparation. This study was also co-designed with three external patient-partners who also identify as young adults with chronic conditions.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".