Designing collaborations involving health technology assessment: discussions and recommendations from the 2024 health technology assessment international global policy forum
Bibliographic record
Abstract
Although collaboration is an intensive way of working together, it is essential for such efforts to achieve shared goals. Health technology assessment (HTA) is transdisciplinary and has an important history of collaboration, with collaboration featuring increasingly in the strategic plans of HTA bodies and stakeholders. Collaboration can be between HTA bodies and between HTA bodies and other stakeholders-most notably regulators but increasingly payers, patient and caregiver organizations, clinicians-clinical societies, and academia. The 2024 HTAi Global Policy Forum (GPF) discussed collaborations involving HTA bodies, reviewing existing and previous collaborations to see what has worked and what can be learned. Core discussion themes included: (i) determining the collaboration purpose is essential but may be dynamic, changing over time; (ii) choosing the collaboration topic takes time, requiring upfront investment and stakeholder mapping; (iii) inviting the right participants and treating them equally is important, including those who can impact HTA, those who will be impacted by HTA and those who bring new information; (iv) collaborations need clear governance, defined roles, responsibilities, metrics, and case study-pilots can be a useful operational model; (v) resourcing collaborations sustainably is a challenge-the time, people, and money required are often under-estimated; (vi) undertaking continual, iterative learning reviews ensures ongoing value and impact of collaborations. Recommendations for future work include the development of a "go/no-go" checklist to determine when collaboration is needed, supplemented with a set of "best practice" principles for establishing and working in collaborations involving HTA bodies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.301 | 0.232 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.031 | 0.016 |
| Scholarly communication | 0.033 | 0.056 |
| Open science | 0.011 | 0.038 |
| Research integrity | 0.045 | 0.039 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".