Bibliographic record
Abstract
This paper examines the funding procedure in Ontario long-term care facilities and seeks to identify whether current resources for protecting the elderly from mistreatment is allocated fairly and effectively. The research also observes how the political economy may influence the needs-based allocation built to protect seniors from mistreatment in institutional care settings and the consequences of these resources on residents’ autonomy. The topic is also viewed through the lens of the current COVID-19 pandemic. Five experts in the area of long-term care participated in this research work including academics, scholars and institutional or agency advocates. Interviews lasting up to 60 minutes interviews were conducted, transcribed and analyzed using a political economy lens. Participants described their knowledge and experience with the funding procedure for long term-care facilities, particularly in Ontario and provided their view on areas that they felt could be improved. The analysis identified four themes including whether the issue is under-resourced, poor allocation of resources; funding according to need; the struggle to define and assess the quality of care; and general work conditions in long-term care. The result of this research will help us to better understand the resource allocation of Ontario long-term care facilities which could in turn highlight improvements that could be made to create better quality of life for residents as well as frontline workers.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".