MétaCan
Menu
Back to cohort
Record W1598884190 · doi:10.1002/hpm.2183

Healthcare reform in Italy: an analysis of efficiency based on nonparametric methods

2013· article· en· W1598884190 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe International Journal of Health Planning and Management · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsnot available
FundersMinistero della SaluteInstitut national de la recherche scientifique
KeywordsData envelopment analysisInefficiencyHealth careDecentralizationProcess (computing)BusinessOperations managementEconomicsComputer scienceEconomic growthStatistics

Abstract

fetched live from OpenAlex

Over the past twenty years, important changes in the Italian health system have led to different approaches in organizing, delivering and financing health services throughout the country's regions. In this paper, we assess the impacts that such changes have had on health efficiency. The analysis performed here is in two stages. In the first stage, healthcare efficiency is measured via bootstrapped Data Envelopment Analysis. In the second stage, the impacts of organizational and environmental variables on efficiency are investigated. Our results highlight that the organizational model adopted by the Lombardia region allows for the best results in healthcare efficiency in Italy. A process of administrative decentralization from the regional governments to local health units appears to be a source of inefficiency. Finally, patient mobility has a significant impact on healthcare efficiency.

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.

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)0.017
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.120
GPT teacher head0.495
Teacher spread0.376 · 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