Beyond Supply Side Fixes: Reducing Magnetic Resonance Imaging Wait Times in Alberta
Bibliographic record
Abstract
Canadian healthcare is plagued by long wait times, especially in magnetic resonance imaging (MRI). Provinces have attempted a variety of policy responses to this issue over the years with minimal success. Most attempts have focused on increasing MRI supply in either the private or public sectors. Past private sector attempts to increase supply have involved offering privately paid and delivered MRI in Alberta and contracting publicly paid MRI to private clinics in Ontario.1 Most recently, Saskatchewan legislated the use of privately paid and delivered MRI, contingent on private clinics performing one publicly paid scan for each private scan.2 As this study finds, these policy responses have not resulted in meaningful MRI wait time reduction. Since increasing private supply has failed to reduce wait times, this study also examines increasing public supply through funding injections. This strategy has also been employed in Alberta, Ontario, and Saskatchewan, as well as at the federal level, to no avail. Since policies to increase MRI supply have largely failed, this study proposes the alternative option of decreasing MRI demand to lower wait times. This study argues that decreasing MRI demand is a more cost-effective and immediate solution to Alberta's continually increasing wait times.3 It also suggests the past provincial policy efforts have contributed to the lack of meaningful wait time reduction and proposes a cohesive national strategy to guide MRI demand reduction policy. The internationally enacted campaign Choosing Wisely is examined as a template for this strategy. This study thus concludes that reducing MRI demand through a campaign such as Choosing Wisely is the best focus of resources to realize meaningful, permanent MRI wait time reduction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".