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Record W2980864246 · doi:10.12927/hcpol.2019.25935

The Introduction of New Non-Drug Health Technologies (NDTs) into Canadian Healthcare Institutions: Opportunities and Challenges

2019· article· en· W2980864246 on OpenAlexaffvenueabout
Tania Stafinski, Raisa Deber, Marc Rhainds, Janet Martin, Tom Noseworthy, Stirling Bryan, Devidas Menon

Bibliographic record

VenueHealthcare policy · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCentre for Advancing Health OutcomesWestern UniversityUniversité LavalUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsHealth careEngineering ethicsEngineeringBusinessEngineering managementPolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: A recent pan-Canadian survey of 48 health organizations concluded that structures, processes, factors and information used to support funding decisions on new non-drug health technologies (NDTs) vary within and across jurisdictions in Canada. METHODS: A self-administered survey was used to determine demographic and financial characteristics of organizations, followed by in-depth interviews with senior leadership of consenting organizations to understand the processes for making funding decisions on NDTs. RESULTS: Seventy-three and 48 organizations completed self-administered surveys and telephone interviews, respectively (with 45 participating in both ways). Fifty-five different processes were identified, the majority of which addressed capital equipment. Most involved multidisciplinary committees (with medical and non-medical representation), but the types of information used to inform deliberations varied. Across all processes, decision-making criteria included local considerations such as alignment with organizational priorities. CONCLUSIONS: NDT decision-making processes vary in complexity, depending on characteristics of the healthcare organization and context.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.484
GPT teacher head0.445
Teacher spread0.039 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations2
Published2019
Admission routes3
Has abstractyes

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