Resource withdrawal from medical services
Bibliographic record
Abstract
Resource withdrawal from unnecessary medical services is an important issue as the cost of health care continues to rise. In many countries, resource withdrawal is primarily determined by government policies that remove, restrict, reduce, or limit the availability of publically insured medical services. Ideally, resource withdrawal is the result of a careful assessment of clinical and economic evidence regarding a service’s safety and effectiveness in order to ensure that it is the most efficient use of resources. Despite advocacy for a routinized and systematic approach to the withdrawal of resources from medical services, research has indicated that political and social factors often influence government, resulting in decisions that are neither consistent nor transparent. In this dissertation I seek to understand factors that may influence resource withdrawal decisions in an attempt to promote a more routinized and systematic approach. In order to understand the resource withdrawal landscape and provide greater conceptual clarity, the first study in this dissertation identifies and explores its characteristics (antecedents, attributes, and outcomes). Definitions of two prominent terms, disinvestment, and rationing are proposed. In the second study, a qualitative analysis of two examples of resource withdrawal reveals how the characteristics of problem frames affect the shape and timing of government resource withdrawal policies. Findings support the proposition that the complexity of the story told within the problem frame affects the shape of the policy; while visibility affects the timing. In the third study, I analyzed the perspectives of key informants about the Choosing Wisely Canada (CWC) campaign, which has the aim of reducing unnecessary services by encouraging a discussion between physician and patient. Findings reveal that CWC was designed to address pressures from government, patients, and the public. However, CWC was not designed in a way that is expected to address the underlying reasons unnecessary services are provided, including limited time in the clinical encounter, patient demands, uncertainty in the care pathway, and physician fear of litigation. Results from all three studies help establish a common language, identify influences on government led resource withdrawal and reasons why CWC is unlikely to reduce unnecessary services. Together this thesis provides insights into some of the factors affecting resource withdrawal from medical services, and findings may be used to help assess ways to improve the formulation of resource withdrawal policies.
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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 | 0.009 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".