Bibliographic record
Abstract
Seniors have outnumbered children in Kingston, Ontario, since 2008. The Community Foundation for Kingston & Area 2017 Vital Signs report projected that the 65+ age cohort would rise to 27% of Kingston’s population by 2026. (CFKA, 2017) Among the many implications that Kingston’s aging population brings is an increased need for health care services. (Statistics Canada, 2024) The demand comes amidst a nation-wide staffing crisis in the healthcare sector that has already impacted the ability of Kingston to meet the needs of its population. Both public and private long-term care facilities have lengthy waitlists, and the city’s two main hospitals are sometimes forced to accommodate individuals waiting for long-term care in acute-care settings. (Schliesmann, 2016) While a wealth of research has made the connection between exposure to nature and mental wellbeing (Cameron et. al., 2020; Grinde, 2009; Hunt, 2022; Verderber, 1987), Ulrich (1984) identified its potential physical benefits when he found that post-operative patients who had views of nature required less potent pain management and recovered more quickly. As it becomes increasingly necessary for Kingston to invest in expanding its health-care infrastructure, the city is presented with an opportunity to create environments of care that incorporate holistic approaches to health through every stage of life, enhance patient care experiences and promote community wellbeing. By exploring global innovations in health architecture that use evidence based design, this paper seeks to build a frame of reference for a future of better care in the city of Kingston.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".