An inclusive, online Delphi process for setting targets for best practice implementation for spinal cord injury
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: The Spinal Cord Injury Knowledge Mobilization Network is a pan-Canadian community of practice composed of seven rehabilitation hospitals. The goal of this network is to utilize implementation science processes to facilitate the adoption of best practice in spinal cord injury (SCI) rehabilitation. In addition to selecting specific practices for implementation, a key aspect of effective implementation is the engagement of stakeholders in decision-making processes. To achieve this, the network utilized a Delphi process to reach consensus on two pressure ulcer prevention and management practices to be implemented in SCI inpatient rehabilitation. A diverse, multidisciplinary panel of clinicians, researchers, sponsoring agency representatives, and persons with SCI participated in this process. METHOD: An online Delphi process was conducted in order to prioritize pressure ulcer prevention and management best practice recommendations and performance indicators for implementation. The process was conducted in six stages: (1) steering committee selection; (2) identification and selection of evidence; (3) participant selection and recruitment; (4) survey development; (5) identification of voting criteria; and (6) five rounds of voting. RESULTS: The Delphi process resulted in the selection of two best practices: (1) comprehensive risk assessment and (2) education for pressure ulcer prevention and management in persons with SCI. CONCLUSIONS: In this Delphi process, a large expert panel achieved consensus on best practice recommendations and associated performance indicators for implementation. This process was undertaken as a first step towards optimization of service delivery and outcomes for persons with SCI across Canada.
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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 | 0.256 | 0.226 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".