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Record W7020618087

Measuring the performance of health care services: a review of international experiences and their application to urban contexts

2006· article· en· W7020618087 on OpenAlexaboutno aff

Bibliographic record

VenueScientific Electronic library online (Sciences Carlos III Health Institute) · 2006
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careGrey literatureQuality (philosophy)Conceptual frameworkBest practicePerformance measurementMEDLINEQuality assessment
DOInot available

Abstract

fetched live from OpenAlex

Background: The objective of performance assessment is to provide governments and populations with appropriate information about the state of their health care system. The objective of this paper is to present the most recent developments in performance assessment and their application in urban contexts. Methods: Literature review in PubMed (1970-2004). We identified additional papers and grey literature from retrieved references. Results: Performance assessment initiatives were identified in Australia, Canada, the United Kingdom, and New Zealand. The World Health Report 2000 is one of the best known examples of a transnational approach to performance assessment. Conclusion: The best developed initiatives to date are those that define precise categories, criteria and indicators with which to analyse and assess health care systems, based on a solid conceptual framework. Performance assessment fits perfectly in urban contexts, as it is a useful tool for designing and monitoring policies, assessing the quality of the services provided, and measuring the health status of city dwellers. Barcelona and Montreal are currently collaborating together on a project to assess the performance assessment of their respective health care 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.359
Teacher spread0.322 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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