Bibliographic record
Abstract
PURPOSE: To present the Shifting Perspectives Model of Chronic Illness, which was derived from a metasynthesis of 292 qualitative research studies. DESIGN: The model was derived from a metasynthesis of qualitative research about the reported experiences of adults with a chronic illness. The 292 primary research studies included a variety of interpretive research methods and were conducted by researchers from numerous countries and disciplines. METHODS: Metastudy, a metasynthesis method developed by the author in collaboration with six other researchers consisted of three analytic components (meta-data-analysis, metamethod, and metatheory), followed by a synthesis component in which new knowledge about the phenomenon was generated from the findings. FINDINGS: Many of the assumptions that underlie previous models, such as a single, linear trajectory of living with a chronic disease, were challenged. The Shifting Perspectives Model indicated that living with chronic illness was an ongoing and continually shifting process in which an illness-in-the-foreground or wellness-in-the-foreground perspective has specific functions in the person's world. CONCLUSIONS: The Shifting Perspectives Model helps users provide an explanation of chronically ill persons' variations in their attention to symptoms over time, sometimes in ways that seem ill-advised or even harmful to their health. The model also indicates direction to health professionals about supporting people with chronic illness.
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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".