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Record W3125991596 · doi:10.12927/hcq.2020.26394

Environmental Sustainability in Canadian Critical Care: A Nationwide Survey Study on Medical Waste Management

2021· article· en· W3125991596 on OpenAlexaffvenueabout
Alec Yu, Iman Baharmand

Bibliographic record

VenueHealthcare Quarterly · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsArtificial Intelligence in Medicine (Canada)Canadian Association of Emergency Physicians
Fundersnot available
KeywordsSustainabilityEcological footprintBusinessHealth careEnvironmental planningBest practiceEnvironmental resource managementEnvironmental healthMedicineGeographyEconomic growthPolitical scienceEcologyEnvironmental science

Abstract

fetched live from OpenAlex

BACKGROUND: To date, the literature surrounding healthcare sustainability has focused largely on operating rooms, energy efficiency and biohazardous waste management. Few studies have looked at the sustainability within intensive care units (ICUs). OBJECTIVE: Our study sought to capture the array of sustainability initiatives undertaken by Canadian ICUs and gain a better understanding of current practices with regard to the management of single-use equipment waste. METHODS: We conducted a nationwide e-mail survey through the Canadian Critical Care Network. RESULTS: We received responses from a total of 81 hospital sites representing all 10 Canadian provinces and approximately 28.3% of all Canadian ICUs. The vast majority of responses came from ICU managers or nursing leadership. Our study identified variable waste management practices across the country and showcased successful initiatives undertaken by Canadian ICUs toward increased environmental sustainability.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.044
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0050.001
Scholarly communication0.0010.001
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 head0.039
GPT teacher head0.355
Teacher spread0.316 · 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

Citations14
Published2021
Admission routes3
Has abstractyes

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