A model of the process of transformation in primary care for people living with chronic illnesses
Bibliographic record
Abstract
OBJECTIVES: The objective of the research was to construct a conceptual framework that explains the process of personal transformation within a primary care context for people living with chronic illnesses. METHODS: A literature search of articles published from 1990 to 2007 in CINAHL, ERIC, Healthstar, MEDLINE, PsycINFO, dissertation abstracts and SocINFO databases, was conducted to identify qualitative research studies investigating personal change in chronic illness or disability. Five hundred and fifty-six articles were identified; the researchers conducted a metasynthesis of a subgroup of these articles whose focus was on transformation in primary care (n = 5). RESULTS: The metasynthesis project presents a model of the conceptualizations of transformation. Transformation in chronic illness is presented as a process of learning about self and the chronic illness in an iterative and continually changing manner. The primary care context has its greatest influence on critical reflection, which occurs in the first phase (initial response). DISCUSSION: This model illustrates the complex processes that occur for someone living with a chronic illness. Health practitioners who understand why and how people transform in living with chronic illnesses will be able to apply this model in determining people's needs and how to assist people during the transformative process.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".