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Record W2107561463 · doi:10.1089/tmj.2005.11.477

A Paradigm Shift in the Evaluation of Information Technology in Health Care

2005· article· en· W2107561463 on OpenAlexafffundabout
Luc Bonneville

Bibliographic record

VenueTelemedicine Journal and e-Health · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversity of Ottawa
FundersMinistère de la Santé et des Services sociaux
KeywordsRestructuringProductivityAmbulatory careInformation technologyHealth careObstacleService (business)EconomicsEconomic growthPublic economicsBusinessPolitical scienceEconomyFinance

Abstract

fetched live from OpenAlex

This paper examines the notion of productivity, which underlies the reorganization of Quebec's health care system around an information technology-driven virage ambulatoire, or shift to ambulatory (outpatient) care. The reorganization may be traced to a policy decision to address the health care's productivity crisis, a crisis diagnosed in the 1970s based on macroeconomic expenditure trend lines highlighting the "obstacle" to economic growth and to balanced budgets. The information technology-driven virage ambulatoire is aimed at a structural reorganization, based on the principle of: boosting productivity in a service- or information-based economy. The State, thus, relied on analytic categories developed for a particular mode of economic analysis to lead a hospital restructuring program oriented around information technology-based ambulatory care. The result was a refocus from clinical and therapeutic efficiency to administrative imperatives of time, cost, and optimization. Hence, Québec moved to neoliberal productivist system in which clinical and therapeutic effectiveness are subordinated to economic imperatives.

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)0.051
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.037
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.008
Science and technology studies0.0040.049
Scholarly communication0.0290.017
Open science0.0030.006
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.408
Teacher spread0.361 · 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 designTheoretical or conceptual
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
Published2005
Admission routes3
Has abstractyes

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