Bibliographic record
Abstract
Canada's 42nd parliament with a Speech from the Throne that was notably short on references to healthcare.About all we heard in that regard was that the government intends to work with the provinces and territories on a new Health Accord.Even if the politicians in Ottawa don't have healthcare at the front of their minds, we at Longwoods -and our readers -certainly do.This edition of Healthcare Quarterly contains nine articles (one third pertaining to chronic disease), a conversation with Louise Bradley (the president and CEO of the Mental Health Commission of Canada), a report from ICES on rheumatoid arthritis surveillance and the results of a CIHI survey on posthospitalization physician follow-up. Delivering Care in the CommunityWe begin with a trio of papers addressing a widespread concern: community care.Terence Montague and his co-authors address the role of non-professional caregivers (i.e., family members or friends) in the care of patients with chronic disease.Drawing on Health Care in Canada surveys, Montague et al. examine this "vital component of the Canadian healthcare system."In particular, they shed light on the personal, physical and financial burdens these individuals carry, and they propose three short-term "opportunities" for improving non-professional caregivers' lot.Anne Wojtak and Joy Klopp next take us through Changing the Conversation, an initiative of the Toronto Central Community Care Access Centre (TC CCAC) aimed at improving the experience of the organization's clients.A "more flexible, conversationfocused approach" is at the heart of Changing the Conversation, which over the last few years has been rolled out across the TC CCAC's in-home personal support, nursing, therapy services and care coordination.Readers will be struck by the measurable benefits for clients this "simple concept" has brought about.The third paper looks at the role of community pharmacists in Manitoba in preventing medication misuse.Using a focusgroup study design, Christine Leong and her fellow researchers identified a long list of factors (e.g., workflow, time, access to health information) that underscore the need for policy and management initiatives aimed at curbing the problem, including improved pharmacist-physician communication and better documentation. Caring for Complex Patients
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.408 | 0.347 |
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".