Measuring Acceptability of Clinical Decision Rules: Validation of the Ottawa Acceptability of Decision Rules Instrument (OADRI) in Four Countries
Bibliographic record
Abstract
BACKGROUND: Clinical decision rules can benefit clinicians, patients, and health systems, but they involve considerable up-front development costs and must be acceptable to the target audience. No existing instrument measures the acceptability of a rule. The current study validated such an instrument. METHODS: The authors administered the Ottawa Acceptability of Decision Rules Instrument (OADRI) via postal survey to emergency physicians from 4 regions (Australasia, Canada, United Kingdom, and United States), in the context of 2 recently developed rules, the Canadian C-Spine Rule (C-Spine) and the Canadian CT Head Rule (CT-Head). Construct validity of the 12-item instrument was evaluated by hypothesis testing. RESULTS: As predicted by a priori hypotheses, OADRI scores were 1) higher among rule users than nonusers, 2) higher among those using the rule ''all of the time'' v. ''most of the time'' v. ''some of the time,'' and 3) higher among rule nonusers who would consider using a rule v. those who would not. We also examined explicit reasons given by respondents who said they would not use these rules. Items in the OADRI accounted for 85.5% (C- Spine) and 90.2% (CT-Head) of the reasons given for not considering a rule acceptable. CONCLUSIONS: The OADRI is a simple, 12-item instrument that evaluates rule acceptability among clinicians. Potential uses include comparing multiple ''protorules'' during development, examining acceptability of a rule to a new audience prior to implementation, indicating barriers to rule use addressable by knowledge translation interventions, and potentially serving as a proxy measure for future rule use.
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 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".