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Record W2990952054 · doi:10.1177/2333393619883605

Motivators and Deterrents to Diet Change in Low Socio-Economic Pakistani Patients With Cardiovascular Disease

2019· article· en· W2990952054 on OpenAlexafffund
Rubina Barolia, Pammla Petrucka, Gina Higginbottom, Faris Farooq Saeed Khan, Alexander M. Clark

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2019
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversity of AlbertaUniversity of Saskatchewan
FundersInternational Development Research Centre
KeywordsSocioeconomic statusSociocultural evolutionDiseaseAffect (linguistics)Environmental healthIntervention (counseling)GerontologyMedicinePsychologyMetropolitan areaFood choicePolitical sciencePsychiatryPopulationPathology

Abstract

fetched live from OpenAlex

This study explores factors that affect the people of low socioeconomic status regarding food choices after diagnosis with cardiovascular disease. Qualitative approach was used to identify the important factors associated with dietary changes as a result of their disease. Twenty-four participants were interviewed from two cardiac facilities in Karachi, the largest metropolitan city of Pakistan. Data were analyzed to identify the themes using the interpretative description approach. While most participants understood the need for dietary changes, few were able to follow recommended diets. Their food choices were primarily influenced by financial constraints as well as cultural, familial, and religious values and practices. The challenge for health care providers lies in understanding the economical, sociocultural, and religious factors that influence behavioral changes which, in turn, affect dietary choices. It is apparent that cardiovascular risk and disease outcomes for the people of low socioeconomic status are likely to escalate. Thus, it is necessary to address the sociocultural, religious, and behavioral factors affecting dietary choices. Achieving this imperative requires an intersectorial, multilevel intervention for the prevention of cardiovascular diseases in people of low socioeconomic status.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.005
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.074
GPT teacher head0.441
Teacher spread0.366 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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