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Record W4368377483 · doi:10.53730/ijhs.v7ns1.14201

Variations in pediatric dental coverage and visits following the implementation of healthcare policies in Pakistan

2023· article· en· W4368377483 on OpenAlexaff
Kashif Adnan, Ahmad Milad Stanikzai, Sadia Yaseen, Hafiz Mahmood Azam, Ammar Ali Khalid, Wali Muhammad Achakzai

Bibliographic record

VenueInternational Journal of Health Sciences · 2023
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsCollège Montmorency
Fundersnot available
KeywordsSocioeconomic statusMedicineDescriptive statisticsInclusion (mineral)Health careDental careEnvironmental healthFamily medicinePromotion (chess)Cross-sectional studyOral healthPublic healthNursingPopulationPsychology

Abstract

fetched live from OpenAlex

Background: In Pakistan, healthcare policies have been implemented to improve children's oral health, including the provision of dental services at primary healthcare centers, the inclusion of dental care services in health insurance schemes, and the promotion of oral health education among the public. However, it is unclear whether these policies have resulted in uniform changes in pediatric dental coverage and visits across different regions and socioeconomic groups. Objective: This study aimed to investigate variations in pediatric dental coverage and visits following the implementation of healthcare policies in Pakistan. Methods: A cross-sectional survey design was used, with a sample size of 200 children aged 0-18 years. Data on pediatric dental coverage and visits were collected through structured interviews with the children and their parents or guardians. The data were analyzed using descriptive statistics and chi-square tests to identify any significant variations in the pediatric dental coverage and visits across different regions and socioeconomic groups. Results: The study found that there were significant variations in pediatric dental coverage and visits across different regions and socioeconomic groups.

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)0.002
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 score0.017
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.027
GPT teacher head0.452
Teacher spread0.424 · 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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