Exploring the role of family history and lay understanding of genetics on the self‐management of disease
Bibliographic record
Abstract
lindsay s (2010) Journal of Nursing and Healthcare of Chronic Illness2, 135–143 Exploring the role of family history and lay understanding of genetics on the self‐management of disease Aims and objectives. This project drew a sample from coronary heart disease registries (n = 108) to explore how people incorporate knowledge of lay genetics and family history of a disease to manage their health. Questionnaires and focus groups were analysed to explore whether having a family history of coronary heart disease (CHD) influenced how patients’ self‐managed their health. Background. Although evidence suggests that genes have an important influence on susceptibility to many diseases, little is known about how people use this knowledge to manage their health. Most research on lay perceptions of genetic causes of disease focuses on people atriskof particular genetic condition, rather than on those who already have a disease. Results. Four key themes emerged from the data that people used to understand their heart disease while incorporating family history in different ways including: (1) being ‘doomed’ because of bad genes; (2) not wanting to change health behaviours; (3) wanting to be careful and lead a healthy lifestyle because of a known family history of heart disease and (4) uncertainty of the role of family history in heart disease. Conclusions. Knowledge of family history of the disease influenced how people experienced health and was inherently embedded within social context to shape new meanings of health. Implications for practice. The recommendations based on this research are to encourage health care providers to develop an understanding of patients’ conception of how their disease was caused so they can give them advice that will fit within their framework. It is important to understand the different ways in which patients conceptualise and respond to their illness.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".