A Scoping Review of Appropriateness of Care Research Activity in Canada from a Health System-Level Perspective
Bibliographic record
Abstract
INTRODUCTION: Jurisdictions are increasingly focusing on appropriate use of healthcare services and interventions as a means to improve health system performance. Our objectives were to conduct a scoping review to (a) map Canadian research and related activity on system-level appropriateness of care and (b) create a resource database that could be used to inform evidence-based decision-making and future research priorities in this area. METHODS: We searched Medline, EMBASE and CINAHL databases between 2003-2013 using terms including "appropriate," "inappropriate," "health technology assessment" and "cost-effectiveness." Articles were included if they were Canadian-based and relevant to our definition. The database search was complemented by a website search of relevant Canadian organizations. RESULTS: 4,979 articles were identified through the literature search, and 103 articles relevant to system-level appropriateness of care across Canada were charted. Of these, 64 contained an evaluation of appropriateness, 30 used a method of cost-effectiveness or total cost impact analysis and 9 involved another methodology. The most common health service categories included drug therapy (n=40) and health service utilization (n=33). Fifty-eight websites were summarized containing material relevant to system-level appropriateness of care. CONCLUSION: Our review identifies Canadian research and related activity pertaining to appropriateness of healthcare from a system-level perspective and provides a useful resource both to support evidence-based decision-making and to guide future appropriateness research.
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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 | 0.064 | 0.202 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.051 | 0.097 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".