Bibliographic record
Abstract
OBJECTIVE: To discuss models of care for frail seniors provided in primary care settings and those developed by Canadian FPs. SOURCES OF INFORMATION: Ovid MEDLINE and the Cochrane database were searched from 2010 to January 2014 using the terms models of care, family medicine, elderly, and geriatrics. MAIN MESSAGE: New models of funding for primary care have opened opportunities for ways of caring for complex frail older patients. Severity of frailty is an important factor, and more severe frailty should prompt consideration of using an alternate model of care for a senior. In Canada, models in use include integrated care systems, shared care models, home-based care models, and family medicine specialty clinics. No one model should take precedence but FPs should be involved in developing and implementing strategies that meet the needs of individual patients and communities. Organizational and remunerative supports will need to be put in place to achieve widespread uptake of such models. CONCLUSION: Given the increased numbers of frail seniors and the decrease in access to hospital beds, prioritized care models should include ones focused on optimizing health, decreasing frailty, and helping to avoid hospitalization of frail and well seniors alike. The Health Care of the Elderly Program Committee at the College of Family Physicians of Canada is hosting a repository for models of care used by FPs and is asking physicians to submit their ideas for how to best care for frail seniors.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".