DEVELOPMENT OF THE ONTARIO DECISION FRAMEWORK: A VALUES BASED FRAMEWORK FOR HEALTH TECHNOLOGY ASSESSMENT
Bibliographic record
Abstract
OBJECTIVES: In 2007, the Ontario Health Technology Advisory Committee (OHTAC) developed a decision framework to guide decision making around nondrug health technologies. In 2012, OHTAC commissioned a revision of this framework to enhance its usability and deepen its conceptual and theoretical foundations. METHODS: The committee overseeing this work used several methods: (a) a priori consensus on guiding principles, (b) a scoping review of decision attributes and processes used globally in health technology assessment (HTA), (c) presentations by methods experts and members of review committees, and (d) committee deliberations over a period of 3 years. RESULTS: The committee adopted a multi-criteria decision-making approach, but rejected the formal use of multi-criteria decision analysis. Three broad categories of attributes were identified: (I) context criteria attributes included factors such as stakeholders, adoption pressures from neighboring jurisdictions, and potential conflicts of interest; (II) primary appraisal criteria attributes included (i) benefits and harms, (ii) economics, and (iii) patient-centered care; (III) feasibility criteria attributes included budget impact and organizational feasibility. CONCLUSION: The revised Ontario Decision Framework is similar in some respects to frameworks used in HTA worldwide. Its distinctive characteristics are that: it is based on an explicit set of social values; HTA paradigms (evidence based medicine, economics, and bioethics/social science) are used to aggregate decision attributes; and that it is rooted in a theoretical framework of optimal decision making, rather than one related to broad social goals, such as health or welfare maximization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".