“I didn’t tell them. Well, they never ask”. Lay understandings of hypertension and their impact on chronic disease management: implications for nursing practice in primary care
Bibliographic record
Abstract
Abstract In the United Kingdom, as in other developed nations, there has been an increased research focus on ethnicity and the mediation of ethnicity on health and illness experience. This paper examines how lay understandings may affect chronic disease management and the steps primary care nurses may take to optimize care delivery for patients/families, using the example of hypertension in the African-Caribbean community. A focused ethnographic approach was adopted for this study. Data were first collected using focus group interviews (2), semi-structured interviews (21), and vignette interviews (5). Data were analyzed with the assistance of Atlas/ti qualitative analysis software using the principles developed by Roper and Shapira. Findings are presented using Kleinman’s seminal work as a theoretical framework: a) the aetiology or cause of the condition, b) the timing and mode of onset, c) the patho-physiological processes involved, d) the natural history and severity of the illness, e) the appropriate treatment for the condition. The paper concludes that it is incumbent upon primary health care nurses to recognize and take account of the lay explanations of health illness that patients/families hold. Failure to do so may compromise effective care-giving.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".