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Record W2606886567 · doi:10.29173/alr770

Efficiency and the Pursuit of Patient Safety

2017· article· en· W2606886567 on OpenAlexvenueno aff

Bibliographic record

VenueAlberta Law Review · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsStaffingGovernment (linguistics)PoliticsFoundation (evidence)BusinessEconomic efficiencyCost efficiencyPatient safetyRisk analysis (engineering)Public economicsLaw and economicsOperations managementHealth careEconomicsMedicineNursingMicroeconomicsLawComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Efficiency and patient safety are two important intersecting health policy goals. Efficiency should not be exploited for political expediency, but instead balanced against concerns with patient safety. Too often, the concept of efficiency has been used to justify cost saving or cost containment measures that create further adverse effects. There is clear evidence that nurseto- patient ratios have a direct impact on patient safety, but the government has resisted staffing more nurses on the basis of cost. This causes a greater cost in terms of human suffering and avoidable medical errors. Therefore, efficiency-based claims made by the government should be viewed with caution. Governments should establish a solid evidentiary foundation before advancing any efficiency claims in the health sector.

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)0.025
metaresearch head score (Gemma)0.043
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 score0.025
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.031
Scholarly communication0.0090.008
Open science0.0010.005
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.035
GPT teacher head0.275
Teacher spread0.239 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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