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Record W2158926844 · doi:10.1016/s0840-4704(10)60139-1

How to Control the Costs of Health Care Services — An Inventory of Strategic Options

2009· article· en· W2158926844 on OpenAlexaff
François Dionne, Craig Mitton, Jean Shoveller, Stuart Peacock

Bibliographic record

VenueHealthcare Management Forum · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsControl (management)BusinessHealth careCost reductionDecision makerHealth servicesFace (sociological concept)Limit (mathematics)PopulationOperations managementInventory managementMarketingRisk analysis (engineering)Operations researchActuarial scienceComputer scienceEconomicsManagement scienceMedicineEnvironmental healthEngineering

Abstract

fetched live from OpenAlex

This paper has two objectives: (1) to provide an inventory of popular strategies for cost reduction or cost containment in the health services research literature and (2) to propose a coherent framework to organize this inventory. The purpose of this framework is to inform decision-makers when grappling with the opposing forces they face in choosing a cost reduction strategy. The trade-off is clear: to access progressively more possible strategies, the decision-maker must be ready to expose the population and patients to more significant changes in services provided. On one hand, more choices are preferable because each strategy attacks the problem from a different angle and being restricted to fewer "angles" increases the likelihood that a specific "well" may have dried up. On the other hand, we know that change is often viewed, a priori, negatively in health care management, so there are pressures to limit the impact on 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 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)0.009
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0020.006
Scholarly communication0.0140.021
Open science0.0030.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0110.001

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 head0.062
GPT teacher head0.300
Teacher spread0.238 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2009
Admission routes1
Has abstractyes

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