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Record W2300085399

Evaluation of Resource Allocation Processes in Canadian Healthcare Organizations: The State of Practice

2012· article· en· W2300085399 on OpenAlexaffvenueabout
Neale Smith, Craig Mitton, Cam Donaldson, Stirling Bryan, Stuart Peacock

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British ColumbiaCanadian Centre for Applied Research in Cancer ControlVancouver Coastal Health
Fundersnot available
KeywordsResource allocationHealth careFunction (biology)Resource (disambiguation)Key (lock)Process (computing)BusinessResource management (computing)Knowledge managementState (computer science)Healthcare systemProcess managementComputer scienceManagementEconomicsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

Resource allocation is a key function of senior leadership teams within healthcare organizations. Academic support of this function has traditionally focused on constructing tools to make decisions more formalized. There has been much less attention paid to understanding resource allocation as a management process. In particular, evaluation has been a missing aspect. The authors conducted a pan-Canadian survey of senior managers within the healthcare system. This survey included questions related to formal evaluation of their resource allocation processes and outcomes. We use this data to shed some light upon the state of priority setting practice in Canada at the present time.

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)0.117
metaresearch head score (Gemma)0.313
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.867

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1170.313
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.017
Science and technology studies0.0070.005
Scholarly communication0.0110.004
Open science0.0050.005
Research integrity0.0010.003
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 head0.390
GPT teacher head0.478
Teacher spread0.088 · 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

Citations1
Published2012
Admission routes3
Has abstractyes

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