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Record W2015967594 · doi:10.12927/whp.2013.23438

Resource Allocation in Pakistan's Health Sector: A Critical Appraisal and a Path toward the Millennium Development Goals

2013· article· en· W2015967594 on OpenAlexvenueno aff
Babar Tasneem Shaikh, Irum Ejaz, Arslan Mazhar, Assad Hafeez

Bibliographic record

VenueWorld health & population · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsMillennium Development GoalsEconomic growthBusinessHealth careGovernment (linguistics)PopulationHealth policyPrivate sectorSocial determinants of healthPovertyEconomicsMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Pakistan is trying hard to sustain its progress toward the Millennium Development Goals. However, because of a lack of political commitment to innovative solutions to improve its financing mechanism, the health system is unable to provide even essential and basic services to the people. The country, with more than 70% of the population living on less than two US dollars a day, largely depends on direct taxes for its revenue. Because of inadequate financing, the quality of government services is inexcusably poor; therefore, a majority of people seek healthcare in the private sector. This has led to a horde of issues pertaining to equity, accessibility and fairness. High out-of-pocket expenses on health jeopardize a family's livelihood, pushing it into a vicious circle of poverty. In the wake of recent devolution, this paper presents options for future health financing that enables the provinces to exert their autonomy to safeguard the health of the most vulnerable in the country. Our recommendations follow the vision of the World Health Organization and the Commission on Macroeconomics and Health, to achieve universal health coverage and social protection for the poor.

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 score0.259
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.052
GPT teacher head0.336
Teacher spread0.284 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

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