Applying Health Belief Model among High Risk Hypertensive Clients
Bibliographic record
Abstract
Background: Worldwide, recent reports indicate that more than one billion adults(more than a quarter of the world’s population) had hypertension, Aim: evaluation the effectof application (HBM) amongst high risk hypertensive clients. Research design: qausiexperimental design was used. Sample: included ten percent of total number 910 of clientswho attended on out-patient clinic in the previous year (214-2015). Setting: the study wasconducted at out patients' clinic of medicine affiliated to the Ain Shams University Hospital.Tools: three tools were used to collect data; the first tool: was a self-administeredquestionnaire for assessing socio-demographic characteristics of clients, and their knowledgeabout hypertension. Second tool: (HBM) related to hypertension. Third tool: physicallyassessment health status of clients measured (BP, pulse, weight, height). Results: studyindicated that more than half of hypertensive clients had unsatisfactory knowledge related tohypertension and near three fifth of them with higher percentage in female. Post health beliefmodel application, there was highly significant improvement in clients knowledge, attitude,and blood pressure. Conclusion: the study proved that used of HBM as framework thatguide and help improvement in client's knowledge, attitude, and health status.Recommendations: the study recommended that design and implement different educationalprograms based on HBM for hypertensive client's and regarding to needs assessment forhypertension (e.g.: definition, classification, causes, signs/symptoms, diagnostic methods,complication, methods of early detection of client's at high risk, management) to improve therate of compliance by improving client's consequences. Counseling hypertensive client'severy time whenever they visit to physician to improve the compliance to antihypertensivedrugs, therapeutic lifestyle modification, and other needed self-cares to control hypertension.Design and disseminate related booklets and brochures to raise client's knowledge regardinghypertension (detect, treating, controlling) as well the community social support network.Utilize different media channels such as T.V., to raise population awareness regardinghypertension, prevention and intervention.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".