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Record W7132863143

Measuring information technology cost efficiency among academic health sciences centres: a benchmarking study

2003· dissertation· W7132863143 on OpenAlexaboutno aff
Lorraine Therese Pederson

Bibliographic record

VenueTSpace · 2003
Typedissertation
Language
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmarkingHealth careInformation technologyPosition (finance)Performance measurementKey (lock)Health information technologyHealth informaticsHealth technology
DOInot available

Abstract

fetched live from OpenAlex

Health care managers are increasingly being questioned about, and must defend their information technology (TI) spending decisions. In 2001, in collaboration with the Chief Information Officers from the Toronto Academic Health Sciences Centres, this cost benchmarking study was initiated. Benchmarking is a useful management tool to compare relative cost position and identify possible action steps. The primary indicator selected for this benchmarking study, the IT spend ratio, is defined as an organization's total IT spending relative to its total operating costs. This study has resulted in a repeatable benchmarking for comparing IT spending levels among health sciences centres but has also highlighted key challenges. The major contribution of this study is a foundation upon which investigations of other aspects of IT performance measurement within health care organizations can be conducted.

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)0.026
metaresearch head score (Gemma)0.065
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.065
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.020
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.305
Teacher spread0.278 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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