Exploring the Lived Experience of Early Hypertension: Insights from Traditional Medicine Perspectives
Bibliographic record
Abstract
OBJECTIVE: To elicit symptoms, risk factors, and habits existing before or right after blood pressure elevation in newly diagnosed patients with hypertension from the traditional Persian medicine perspective.METHODOLOGY: This was a concurrent nested mixed-method study conducted in 2018.We included newly diagnosed cases of hypertension (BP≥140/90 mmHg in two consecutive screenings) in the study.In contrast, those who had a history of hypertension or used anti-hypertension medication were excluded.The participants were surveyed and interviewed to identify their temperament and extract their recent experiences with hypertension.The sampling followed the criterion-based purposive technique, and the sample size was defined based on qualitative data saturation.The recorded interviews were transcribed and coded according to Persian medicine until no new code emerged.RESULTS: Twenty participants were interviewed, and two themes were extracted: (i) primary or predisposing factors, such as warm temperament, change of residence, improper eating habits, abrupt cessation of exercise, psychological factors, and irregular sleep patterns, and (ii) early symptoms that occur at the first sign of rising blood pressure, including digestive complaints and changes in body excretion, psychological manifestations, and unclassifiable general symptoms.CONCLUSION: Physicians are advised to pay attention to these items when taking a history from patients to prevent hypertension and treat it at its early stages.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".