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Record W2592791446 · doi:10.1177/0840470416677118

Greening healthcare at Muskoka Algonquin Healthcare

2017· article· en· W2592791446 on OpenAlex
Debra Stone

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealthcare Management Forum · 2017
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Environmental Waste Management
Canadian institutionsCARE Canada
Fundersnot available
KeywordsBusinessWork (physics)Health carePlan (archaeology)Promotion (chess)LaundryEnvironmental planningOperations managementWaste managementEngineeringEconomic growthEnvironmental sciencePolitical scienceGeography

Abstract

fetched live from OpenAlex

Waste diversion is fundamental to reducing the ecological footprint. Until 2012, waste generated by Muskoka Algonquin Healthcare (MAHC) was not incorporated into any formal waste diversion efforts. In 2012, the Reduce, Recycle, Waste Diversion Program was initiated. Support for the program was endorsed by the senior leadership team, staff, and the community, and incorporated into the strategic plan, which was instrumental in the program's success. The goal of the waste diversion program was to help MAHC work towards a sustainable future and make MAHC a leading hospital in making responsible environmental choices. By increasing the number of recycle stations at MAHC's two hospital sites and providing education and promotion on the importance of waste diversion, MAHC has been successful in reducing the amount of waste going to the landfill to a 48% level between 2012 and 2015. The following case study illustrates and discusses MAHC's successful waste diversion efforts.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.040
GPT teacher head0.331
Teacher spread0.291 · 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