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Record W4389320366 · doi:10.35484/ahss.2023(4-iv)04

Prevalence of Anemia and its Determinants among the Rural Women of Khyber Pakhtunkhwa-Pakistan

2023· article· en· W4389320366 on OpenAlexaff
Shaista Naz, Mahnoor Aslam, Aniqa Sayed

Bibliographic record

VenueAnnals of Human and Social Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAnemiaSocioeconomic statusKhyber pakhtunkhwaRespondentMedicinePsychological interventionPublic healthEnvironmental healthRural areaPromotion (chess)Health promotionGerontologySocioeconomicsPopulationNursingPolitical science

Abstract

fetched live from OpenAlex

Current study investigated prevalence of anemia and its determinants among the rural women of Khyber Pakhtunkhwa-Pakistan. Anmeia is a major public health concern in the country, however there is a lack of research studies in the rural settings. For the prevalence of anemia, 100 respondents provided blood samples and a questionnaire was also filled from each respondent. For the determinants of anemia, key informant interviews (10) from the lady doctors were conducted. It was found that more than half of the respondents were anemic as their hemoglobin was <12.0 g/dl. Socioeconomic status, cultural and gender norms, dietary factors, reproductive factors, and other factors were the important determinants of anemia among the rural women. The study calls for the developmental interventions like improved access to iron-rich foods, promotion of education and awareness, enhancement of healthcare services, and addressing of gender disparities in the study area to combat anemia.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.385
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2023
Admission routes1
Has abstractyes

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