Politics and its intersection with coverage with evidence development: a qualitative analysis from expert interviews
Bibliographic record
Abstract
BACKGROUND: Pressures on health care budgets have led policy makers to discuss how to balance the provision of costly technologies to populations in need and making coverage decisions under uncertainty. Coverage with evidence development (CED) is being employed to meet these challenges. METHODS: Twenty-four interviews were carried out between June 2009 and December 2010 with researchers, decision makers and policy makers from Australia, Canada, United Kingdom and United States. Three phases of coding occurred, the first being manual coding where the interviews were read and notes were taken and nodes were extracted and imputed. NVIVO coding was applied to the interview transcripts, with both broad general searches for word usages and imputed nodes. RESULTS: Four overarching thematic areas emerged out of contextual analysis of the interviews - (1) what constitutes CED; (2) the lack of a systematic approach/governance structure; (3) the role of the pharmaceutical industry and overt political considerations in CED; and (4) alternatives and barriers to CED. We explore these themes and then use concrete examples of CED projects in each of the four countries to illustrate the political issues that our interviewees raised. CONCLUSION: Until the underlying political nature of CED is recognized then fundamental questions about its usefulness and operation will remain unresolved.
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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 | 0.051 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".