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Record W2527564487 · doi:10.21149/spm.v58i5.8242

Asignación financiera en el Sistema de Protección Social en Salud de México: retos para la compra estratégica

2016· article· es· W2527564487 on OpenAlexaff
Miguel Ángel González-Block, Alejandro Figueroa, Ignacio García-Téllez, José L. Alarcón

Bibliographic record

VenueSalud Pública de México · 2016
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE:: The financial coordination of the System of Social Protection in Health (SPSS) was analyzed to assess its support to strategic purchasing. MATERIALS AND METHODS:: Official reports and surveys were analyzed. RESULTS:: SPSS covers a capita of 2 765 Mexican pesos, equivalent to 0.9% of GDP. The Ministry of Health contributed 35% of the total, state governments 16.7% and beneficiaries 0.06%. The National Commission for Social Protection in Health received 48.3% of resources, allocating 38% to State Social Protection Schemes in Health and paying 7.4% of the total directly to providers.The state contribution is in deficit while family contributions tend not to be charged. CONCLUSION:: SPSS has not built funds specialized in strategic purchasing, capable of transforming historical budgets.The autonomy of providers is key to reduce out-of-pocket spending through the supply of quality services.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.030
GPT teacher head0.291
Teacher spread0.261 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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