Strength in Numbers: An Analysis of Team-Based Primary Care in Canada
Bibliographic record
Abstract
The COVID-19 pandemic has served to amplify a plethora of pre-existing shortcomings with Canadian healthcaresubjecting these issues to extreme public scrutiny in the wake of an overburdened system.When addressing these concerns, it is critical to recognize that there is no onesize-fits-all change to remedy such problems.Rather, it is important to adopt a multifaceted, goal-oriented approach through which healthcare can be improved across a variety of parameters.High quality healthcare in Canada is defined as timely, effective, efficient, equitable, and patient centred [1] -by focusing on these parameters healthcare professionals, policy makers, and other key stakeholders can work towards the betterment of Canadian healthcare.One such piece of this issue involves investment in primary care.This does not simply entail an increase in funding, but also requires pursuing necessary infrastructure changes.More specifically, this commentary delves into the expansion of team-based primary care and how it can be implemented to tackle some of the key shortcomings facing the Canadian healthcare system.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.025 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".