Commonalities and differences in the implementation of models of care for arthritis: key informant interviews from Canada
Bibliographic record
Abstract
BACKGROUND: Timely access to effective treatments for arthritis is a priority at national, provincial and regional levels in Canada due to population aging coupled with limited health human resources. Models of care for arthritis are being implemented across the country but mainly in local contexts, not from an evidence-informed policy or framework. The purpose of this study is to examine existing models of care for arthritis in Canada at the local level in order to identify commonalities and differences in their implementation that could point to important considerations for health policy and service delivery. METHODS: Semi-structured key informant interviews were conducted with 70 program managers and/or care providers in three Canadian provinces identified through purposive and snowball sampling followed by more detailed examination of 6 models of care (two per province). Interviews were transcribed verbatim and analyzed thematically using a qualitative descriptive approach. RESULTS: Two broad models of care were identified for Total Joint Replacement and Inflammatory Arthritis. Commonalities included lack of complete and appropriate referrals from primary care physicians and lack of health human resources to meet local demands. Strategies included standardized referrals and centralized intake and triage using non-specialist health care professionals. Differences included the nature of the care and follow-up, the role of the specialist, and location of service delivery. CONCLUSIONS: Current models of care are mainly focused on Total Joint Replacement and Inflammatory Arthritis. Given the increasing prevalence of arthritis and that published data report only a small proportion of current service delivery is specialist care; provision of timely, appropriate care requires development, implementation and evaluation of models of care across the continuum of care.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".