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Record W4401195039 · doi:10.53555/sfs.v10i3.2918

Study To Assess Knowledge And Attitude Regarding Hypertension, Compliance To Treatment And Control Measures Adopted By Hypertensive Clients

2023· article· en· W4401195039 on OpenAlexvenueno aff
Ramandeep Kaur

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDiverse Scientific Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCompliance (psychology)Control (management)PsychologyApplied psychologyMedicineSocial psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Hypertension is s silent killer increasing day by day. The objective of the study was to assess the Knowledge and attitude, compliance to treatment and control measures of hypertensive clients regarding hypertension.200 clients suffering from hypertension, were selected through purposive sampling technique. In view of the nature of the problem and to accomplish the objectives of the study, structured knowledge questionnaire and attitude scale and compliance scale and observational checklist was prepared and Reliability of the tools was tested by Kr20 for knowledge questionnaire, cronbach’s alpha for attitude scale and compliance to treatment scale and inter-rated reliability for control measures was used, which was 0.76 for knowledge, 0.78 for attitude and 0.86 for compliance and 0.7 for control measures respectively. From the study findings, it was found that the hypertensive clients had fair Knowledge regarding hypertension. The hypertensive clients had favorable attitude regarding prevention and control of hypertension. The hypertensive clients have Good compliance to treatment regarding hypertension. The hypertensive clients are having fair control of hypertension.  The findings suggest that hypertensive clients are having fair knowledge, favorable attitude regarding hypertension, good compliance but they are having fair control over hypertension.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.659
GPT teacher head0.479
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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