Defining the effect and mediators of two knowledge translation strategies designed to alter knowledge, intent and clinical utilization of rehabilitation outcome measures: a study protocol [NCT00298727]
Bibliographic record
Abstract
BACKGROUND: A substantial number of valid outcome measures have been developed to measure health in adult musculoskeletal and childhood disability. Regrettably, national initiatives have merely resulted in changes in attitude, while utilization remains unacceptably low. This study will compare the effectiveness and mediators of two different knowledge transfer (KT) interventions in terms of their impact on changing knowledge and behavior (utilization and clinical reasoning) related to health outcome measures. METHOD/DESIGN: Physical and occupational therapists (n = 144) will be recruited in partnership with the national professional associations to evaluate two different KT interventions with the same curriculum: 1) Stakeholder-Hosted Interactive Problem-Based Seminar (SHIPS), and 2) Online Problem-Based course (e-PBL). SHIPS will consist of face-to-face problem-based learning (PBL) for 2 1/2 days with outcome measure developers as facilitators, using six problems generated in consultation with participants. The e-PBL will consist of a 6-week web-based course with six generic problems developed by content experts. SHIPS will be conducted in three urban centers in Canada. Participants will be block-allocated by a minimization procedure to either of the two interventions to minimize any prognostic differences. Trained evaluators at each site will conduct chart audits and chart-stimulated recall. Trained interviewers will conduct semi-structured interviews focused on identifying critical elements in KT and implementing practice changes. Interviews will be transcribed verbatim. Baseline predictors including demographics, knowledge, attitudes/barriers regarding outcome measures, and Readiness to Change will be assessed by self-report. Immediately post-intervention and 6 months later, these will be re-administered. Primary qualitative and quantitative evaluations will be conducted 6-months post-intervention to assess the relative effectiveness of KT interventions and to identify elements that contribute to changing clinical behavior. Chart audits will determine the utilization of outcome measures (counts). Incorporation of outcome measures into clinical reasoning will be assessed using an innovative technique: chart-stimulated recall. DISCUSSION: A strategy for optimal transfer of health outcome measures into practice will be developed and shared with multiple disciplines involved in primary and specialty management of musculoskeletal and childhood disability.
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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 |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
| grok | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
| opus | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | medium |
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 | 0.083 | 0.068 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.011 |
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 3 models reading the full record.
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".