A systematic review of interventions to increase the use of standardized outcome measures by rehabilitation professionals
Bibliographic record
Abstract
OBJECTIVE: To determine the types and effectiveness of interventions to increase the knowledge about, attitudes towards, and use of standardized outcome measures in rehabilitation professionals. DATA SOURCES: An electronic search using Medline, EMBASE, PsycINFO, CINAHL, Ergonomics Abstracts, Sports Discus. The search is current to February 2016. STUDY SELECTION: All study designs testing interventions were included as were all provider and patient types. Two reviewers independently conducted a title and abstract review, followed by a full-text review. DATA EXTRACTION: Two reviewers independently extracted a priori variables and used consensus for disagreements. Quality assessment was conducted using the Assessment of Quantitative Studies published by the Effective Public Health Practice Group. DATA SYNTHESIS: We identified 11 studies involving at least 1200 providers. Nine of the studies showed improvements in outcome measure use rates but only three of these studies used an experimental or quasi-experimental design. Eight of the studies used an educational approach in the intervention and three used audit and feedback. Poor intervention description and quality of studies limited recommendations. CONCLUSIONS: Increased attention to testing interventions focused on known barriers, matched to behavior change techniques, and with stronger designs is warranted.
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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 | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".