Development of theory‐based knowledge translation interventions to facilitate the implementation of evidence‐based guidelines on the early management of adults with traumatic spinal cord injury
Bibliographic record
Abstract
RATIONALE: Optimal, early management following a spinal cord injury (SCI) can limit individuals' disabilities and costs related to their care. Several knowledge syntheses were recently published to guide health care professionals with regard to early interventions in SCI patients. However, no knowledge translation (KT) intervention, selected according to a behaviour change theory, has been proposed to facilitate the use of SCI guidelines in an acute care setting. OBJECTIVES: To develop theory-informed KT interventions to promote the application of evidence-based recommendations on the acute care management of SCI patients. METHODS: The first four phases of the knowledge-to-action model were used to establish the study design. Knowledge selection was based on the Grading of Recommendations Assessment, Development and Evaluation system. Knowledge adaptation to the local context was sourced from the ADAPTE process. The theoretical domains framework oriented the selection and development of the interventions based on an assessment of barriers and enablers to knowledge application. RESULTS: Twenty-nine recommendations were chosen and operationalized in measurable clinical indicators. Barriers related to knowledge, skills, perceived capacities, beliefs about consequences, social influences, and the environmental context and resources theoretical domains were identified. The mapping of behaviour change techniques associated with those barriers led to the development of an online educational curriculum, interdisciplinary clinical pathways as well as policies and procedures. CONCLUSIONS: This research project allowed us developing KT interventions according to a thorough behavioural change methodology. Exposure to the generated interventions will support health care professionals in providing the best care to SCI patients.
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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 | 0.035 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".