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Record W2942482445

Free health care for the poor: a good way to achieve universal health coverage? Evidence from Morocco

2018· preprint· en· W2942482445 on OpenAlexaboutno aff
Raphaël Cottin

Bibliographic record

VenueRePEc: Research Papers in Economics · 2018
Typepreprint
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careQuarter (Canadian coin)BusinessPopulationHealth policyUser feePublic economicsQuality (philosophy)Economic growthDemographic economicsEconomicsEnvironmental healthMedicinePolitical scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

Policies and programs aimed at giving access to health care free of charge for some segments of the population are increasingly being put in place by low- and middle income countries, going against the Washington-consensus era recommendation to impose user fees on public health care to insure a better quality of service. Yet, such policies may not be suited for middle-income countries, where user fees are not necessarily be the biggest barriers to health care. We study a nationwide example of such a policy with the generalization of the Medical Assistance Regime or RAMED in the Kingdom of Morocco, a policy aiming at giving free access to hospital care to the poorest quarter of the population. Using nationally representative panel data collected before, during and after the extension of the policy, we show that the removal of user fees did have a positive impact on access to health care, but that this impact comes mostly from poorer, rural households. We also study the impact on health expenditures, and find that it has not led to a decrease of the financial burden, except for the subset of urban households that have recurring health expenditures. Overall, our result show that in a middle income country , user fees, even modest, seem to significantly deter healthcare usage for the rural part of the population.

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)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0030.005
Research integrity0.0010.005
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 head0.096
GPT teacher head0.469
Teacher spread0.373 · 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 designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

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