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Record W3152679885

Hospital Performance Measuring

2004· article· en· W3152679885 on OpenAlexaboutno aff
Akram Barati, R Khalilnezhad

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2004
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Introduction: The core requirement of successful and innovative organizations is doing right things and doing things right. Nowadays organizations are to perform excellently strategically and operationally so that they can face the current and future world challenges. Performance measurement is one of the ways in directing organization to the right targets and avoid diversity in practices. The effective performance measurement will result in accountability and responsiveness, thus making it possible to maximize the utilization of limited available resources. In this paper some of the dimensions and frameworks for performance measurement are presented and reviewed. Then a conceptual framework has been recommended for determining performance dimensions and indicators. Methods: In this paper after reviewing a brief history of performance measurement, and the characteristics of traditional models of performance measurement, including BSC and model in hospitals founded by National Health System, and Canadian hositals and RDF model of Montreal University, a model used on Denmark, quality indicator rojects in U.S, and the framework were studied. Literature Review: Following a brief history at performance measurement, and performance measurement frame works and models at performance in health core settings and hospital including Base model in hospitals founded by national health system, Canadian hospitals and RDF model, of Montreal university, a model used in Denmark, Quality indicator, projects in U.S, and the from work for the evaluation at NHS performance and the performance dimensions in each frame work were studied. Result: Reviewing models and frameworks based on the available foundations indicate that each of them has strengths and weaknesses. One of the reasons is the multiplicity of hospital functions and health policies. Yet in measuring hospital performance non of the functional areas should be ignored. There fore when reviewing the internal affairs of the hospitals, organizational, and theoretic specifications of hospitals should be fully understood to have a comprehensive analyses of the hospital performance. At the end of the study a conceptual frame work is presented on the account of patient centered strategies of hospitable whishes necessitate data measurement, performance processes, organizal structure, and most important organization results, so that we can come up with a lanead and complete view of a hospital performance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.038
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.015
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0300.010

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 head0.473
GPT teacher head0.631
Teacher spread0.158 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2004
Admission routes1
Has abstractyes

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