From illness management to quality of life: rethinking consumer health informatics opportunities for progressive, potentially fatal illnesses
Bibliographic record
Abstract
OBJECTIVES: Investigate how people with chronic obstructive pulmonary disease (COPD)-an example of a progressive, potentially fatal illness-are using digital technologies (DTs) to address illness experiences, outcomes and social connectedness. MATERIALS AND METHODS: A transformative mixed methods study was conducted in Canada with people with COPD (n = 77) or with a progressive lung condition (n = 6). Stage-1 interviews (n = 7) informed the stage-2 survey. Survey responses (n = 80) facilitated the identification of participants for stage-3 interviews (n = 13). The interviews were thematically analyzed. Descriptive statistics were calculated for the survey. The integrative mixed method analysis involved mixing between and across the stages. RESULTS: Most COPD participants (87.0%) used DTs. However, few participants frequently used DTs to self-manage COPD. People used DTs to seek online information about COPD symptoms and treatments, but lacked tailored information about illness progression. Few expressed interest in using DTs for self- monitoring and tracking. The regular use of DTs for intergenerational connections may facilitate leaving a legacy and passing on traditions and memories. Use of DTs for leisure activities provided opportunities for connecting socially and for respite, reminiscing, distraction and spontaneity. DISCUSSION AND CONCLUSION: We advocate reconceptualizing consumer health technologies to prioritize quality of life for people with a progressive, potentially fatal illness. "Quality of life informatics" should focus on reducing stigma regarding illness and disability and taboo towards death, improving access to palliative care resources and encouraging experiences to support social, emotional and mental health. For DTs to support people with fatal, progressive illnesses, we must expand informatics strategies to quality of 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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".