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Record W4282966714 · doi:10.53730/ijhs.v6ns5.9042

Access to health care among rural population

2022· article· en· W4282966714 on OpenAlexaboutno aff
Rekharani Sethy, Somanath Sethi, Sujata Sethi, Shree Kumar Chinmayananda Mishra

Bibliographic record

VenueInternational Journal of Health Sciences · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsAsset (computer security)PopulationSocioeconomicsEnvironmental healthEconomic growthHealth careQuarter (Canadian coin)GerontologyMedicineGeographyPsychologySociologyEconomicsComputer scienceComputer security

Abstract

fetched live from OpenAlex

Background: Good health is very important to human productivity and the “development” process, economic and technological development of the individual as well as for the nation. A healthy community is an asset to nation. The progress of nation is measured by health of its people because healthy people can contribute for the betterment and progress of the nation. Objectives: Toassess the existing status, adequacy and utilization of health services under NRHM in the selected areas in Mayurbhanj & Jagatsinghpur district of the state, Methodology: This is a mixed-method study conducted at Erasama block of Jagatsinghpur District & Baripada Block of Mayurbhanj district in Odisha. Both Primary and secondary data has been used for the study. As a part of qualitative component, n-depth interview and Focussed group discussion were conducted among a subgroup of study participants. Study was conducted from April-2018 to March -2019inAmbiki village and Rangamatia of Erasama and Baripada Blocks respectively. Findings of study: Most of the ASHAs are providing services to a population of more than the specified standard of 1,000. Transportation ofantenatal mothers was found to be a major problem. The total compensation ASHAs receive for each month was comparatively low.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.149
GPT teacher head0.582
Teacher spread0.433 · 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.

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
Published2022
Admission routes1
Has abstractyes

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