20th Anniversary Ottawa Decision Support Framework: Part 3 Overview of Systematic Reviews and Updated Framework
Bibliographic record
Abstract
Introduction. The Ottawa Decision Support Framework (ODSF) has guided practitioners and patients facing difficult decisions for 20 years. It asserts that decision support interventions that address patients’ decisional needs improve decision quality. Purpose. To update the ODSF based on a synthesis of evidence. Methods. We conducted an overview of systematic reviews, searching 9 electronic databases. Eligible reviews included decisional needs assessments, decision support interventions, and decisional outcome measures guided by the ODSF. We extracted data and synthesized results narratively. Eight ODSF developers/expert users from 4 disciplines revised the ODSF. Results. Of 4656 citations, we identified 4 eligible reviews (>250 studies, >100 different decisions, >50,000 patients, 18 countries, 5 continents). They reported current ODSF decisional needs and their most frequent manifestations in the areas of inadequate knowledge/information, unclear values, decisional conflict/uncertainty, and inadequate support. They uncovered 11 new manifestations of 6 decisional needs. Using the Decisional Conflict Scale (DCS) to assess decisional needs, average scores were elevated at baseline and declined shortly after decision making, even without information interventions. Patient decision aids were superior to usual care in reducing total DCS scores and improving decision quality. We revised the ODSF by refining definitions of 6 decisional needs and adding new interventions to address 4 needs. We added a decision process outcome and eliminated secondary outcomes unlikely to improve across a range of decisions, retaining the implementation/continuance of the chosen option and appropriate use/costs of health services. Conclusions. We updated the ODSF to reflect the current evidence and identified implications for practice and further research.
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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 | 0.114 | 0.209 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.023 |
| Bibliometrics | 0.075 | 0.034 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.013 | 0.013 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 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, 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".