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Record W2120697137 · doi:10.12927/hcpol.2012.23015

Reforms in Pakistan: Decisive Times for Improving Maternal and Child Health

2012· article· en· W2120697137 on OpenAlexvenueno aff
Arslan Mazhar, Babar Tasneem Shaikh

Bibliographic record

VenueHealthcare policy · 2012
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsDisadvantagedEconomic growthBusinessEquity (law)Health careMillennium Development GoalsWorkforceHealth equityDistribution (mathematics)Environmental healthMedicineDeveloping countryPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Pakistan is a struggling economy with poor maternal and child health indicators that have affected attainment of the United Nations Millennium Development Goals 4 and 5 (under-five child and maternal mortality). Recent health reforms have abolished the federal Ministry of Health and devolved administrative and financial powers to the provinces. Ideally, devolution tends to simplify a healthcare system's management structure and ensure more efficient delivery of health services to underserved populations, in this case women and children. In this time of transition, it is appropriate to outline prerequisites for the efficient management of maternal and child health (MCH) services. This paper examines the six building blocks of health systems in order to improve the utilization of MCH services in rural Pakistan. The targeted outcomes of recent reforms are devolved participatory decision-making regarding distribution of MCH-related services, improved deployment of the healthcare workforce, prioritization of pro-poor strategies for health financing and integration of various health information systems. Given this window of opportunity, the provinces need to guarantee fairness and equity through their stewardship of the healthcare system so as to protect vulnerable mothers and their children, especially in rural, remote and disadvantaged areas of Pakistan.

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)0.000
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 score0.298
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.382
Teacher spread0.363 · 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

Citations17
Published2012
Admission routes1
Has abstractyes

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