Occupational disruption during a pandemic: Exploring the experiences of individuals living with chronic disease
Bibliographic record
Abstract
The aim of this study was to explore the experiences of occupational disruption during a pandemic among individuals living with chronic disease, with particular attention to experiences related to managing disease(s) and routine healthcare. This qualitative descriptive study using virtual semi-structured interviews was conducted with 23 participants diagnosed with chronic disease(s) for at least 2 years, living in New Brunswick, Canada. Three overarching themes, and seven sub-themes emerged from the thematic analysis: 1) reactions (sub-themes: occupational loss and loneliness; fear and vulnerability); 2) adaptations (sub-themes: engaging in new or re-discovered occupations; prioritizing self-management; changing perspective); and 3) barriers (sub-themes: limited access to care; environmental challenges). Individuals living with chronic disease modified what they did to provide structure and purpose in response to restrictions imposed on their regular day-to-day occupations and the feelings this evoked. Notably, participants reported a heightened engagement in occupations focused on maintaining health and well-being; however, not all adaptations resulted in positive outcomes. Reactions such as fear and vulnerability influenced behavior and contributed to apprehension to seek out medical care. Changes in the environment also created barriers and challenged the participants’ ability to adapt. This research offers the first glimpse inside this occupational paradigm, to not only enhance understanding of the impact of COVID-19 on individuals living with chronic disease, but also to strengthen knowledge of what is needed to improve communication, organization, and the provision of care for individuals living with chronic disease both during a pandemic and in everyday life.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.009 |
| 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".