Happenings / L'Événement : Focus on Health-Care Settings: The Home Care Evaluation and Research Centre
Bibliographic record
Abstract
Health system restructuring in Canada has involved a dramatic shift towards the delivery of ambulatory, home-based, and, more recently, Internet-based health care. This dispersion of health-care services represents one of the most significant social changes of the last two decades and will continue to have major repercussions in the new century. Although restructuring has been rapid and ubiquitous, systematic economic evaluations of non-traditional health-care services and delivery settings have been lacking in Canada. This absence of evidence limits opportunities to measure effectiveness and impedes decision-making. To fill this gap, the Home Care Evaluation and Research Centre (HCERC) was launched in 1998 at the University of Toronto, with public and private sector funding of more than $1.2 million. HCERC's Co-Directors are Patricia McKeever (Faculty of Nursing) and Peter Coyte (Department of Health Administration, Faculty of Medicine). HCERC's goals are to facilitate collaborative research related to the settings where health care is sought, delivered, and received, and to support knowledge transfer and linkage activities throughout the research process.
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 | 0.124 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".