Investigating the integration of sustainable food initiatives in healthcare institutions in Ontario, Canada: A grey literature scoping review protocol
Bibliographic record
Abstract
Abstract Introduction The climate emergency and other sustainability challenges interact to threaten human and planetary health. Efforts to improve the sustainability of food initiatives within healthcare institutions could mitigate these threats by addressing the four pillars of sustainability: health, social, economic, and environmental. Understanding current initiatives to incorporate sustainability into food programs and the sustainability pillars that guide those initiatives is important to inform priorities for action. This scoping review was undertaken to investigate the extent to which major healthcare institutions in Ontario, Canada have publicly committed to, discussed, planned, and/or implemented sustainable food initiatives. Methods and Analysis Steps are based on guidance from the Joanna Briggs Institute and Arksey & O’Malley. First, the current strategic plans of 57 healthcare institutions in Ontario, Canada, will be retrieved from their websites and used to examine whether they include any commitments to or discussion, planning, and/or implementation of relevant initiatives. The healthcare institution websites, along with those of selected sustainability organizations, will be searched for grey literature from 2015 to 2024 describing sustainable food initiatives within these institutions. Documents will be screened for eligibility by two researchers. Data related to the incorporation of sustainable food into institutional food programs, and the sustainability pillars addressed, will be extracted by one researcher, with 10% of entries verified by a second researcher. The data will be synthesized to summarize publicly reported progress toward integrating sustainable food into healthcare institutions. Ethics and Dissemination This review will use publicly available grey literature with no expectation of privacy and no research participants; therefore, no ethics clearance is required. Results will be shared with stakeholders in sustainability organizations, as well as at relevant conferences and in peer-reviewed journals, such as the Healthy Cities Conference and the Journal of Canadian Food Studies. This protocol is registered on the Open Science Framework and can be accessed at the following URL: https://doi.org/10.17605/OSF.IO/CU9P6
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.118 | 0.117 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.031 | 0.027 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".