Adaptive Evolution in Rapid Assessments: A 25-Year Perspective
Bibliographic record
Abstract
OBJECTIVES: This article retrospectively examines the evolution of rapid assessments (RAs) produced by the Health Technology Assessment (HTA) Program at the Institute of Health Economics over its 25-year relationship with a single requester, the Alberta Health Ministry (AHM). METHODS: The number, types, and methodological attributes of RAs produced over the past 25 years were reviewed. The reasons for developmental changes in RA processes and products over time were charted to document the push-pull tension between AHM needs and the HTA Program's drive to meet those needs while responding to changing methodological benchmarks. RESULTS: The review demonstrated the dynamic relationship required for HTA researchers to meet requester needs while adhering to good HTA practice. The longstanding symbiotic relationship between the HTA Program and the AHM initially led to increased diversity in RA types, followed by controlled extinction of the less fit (useful) "transition species." Adaptations in RA methodology were mainly driven by changes in best practice standards, requester needs, the healthcare environment, and staff expertise and technology. CONCLUSIONS: RAs are a useful component of HTA programs. To remain relevant and useful, RAs need to evolve according to need within the constraints of HTA best practice.
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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 | 0.069 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".