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Change in the Population of Health Systems: From 1985 to 1998

2001· article· en· W2474058823 on OpenAlexaff
Melissa J. Succi, Jeffrey A. Alexander, Shoou-Yih D. Lee

Bibliographic record

VenueJournal of Healthcare Management · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsPopulationHealthcare systemPopulation healthCorporate governanceSocial systemHealth careSociologyEconomicsEconomic growthDemographyManagementSocial science

Abstract

fetched live from OpenAlex

This article compares the predictions in Stephen Shortell's 1988 seminal article, The Evolution of Hospital Systems: Unfulfilled Promises and Self-Fulfilling Prophesies, with current data on health systems over a 14-year period from 1985 to 1998. Specifically, we review five of Shortell's predictions related to the horizontal growth of health systems and compare these predictions with empirical data on structural changes in the population of health systems. Our analyses suggest that Shortell's predictions corresponded to much of the actual behavior demonstrated in the population over the past one-and-a-half decades. Support was found for the following: (1) health systems form in two recurring stages; (2) previously unaffiliated hospitals are affiliating with existing systems rather than participating in the creation of new systems; and (3) health systems have evolved into five different strata, each of which represents different shares of the population; such population patterns have important implications for individual hospitals and health systems. By attending to patterns of change in the industry's social structure, hospitals and health systems can determine whether it is likely to continue along past trajectories or whether it shows signs of change that may pave way for the breakdown of existing organizational forms, entry of new organizational players, and the emergence of new governance structures.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.660
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.128
GPT teacher head0.338
Teacher spread0.210 · 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.

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

Citations6
Published2001
Admission routes1
Has abstractyes

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