Examining the Feasibility and Effectiveness of a Community-Based Organization Implementing an Event-Based Knowledge Mobilization Initiative to Promote Physical Activity Guidelines for People With Spinal Cord Injury Among Support Personnel
Bibliographic record
Abstract
UNLABELLED: Community-based organizations (CBOs) and support personnel that serve marginalized members of society have the potential to be important partners in knowledge mobilization (KM). A CBO in partnership with researchers developed an event-based KM initiative to disseminate evidence-based physical activity guidelines for people with spinal cord injury. PURPOSE: The purpose of this case study is to demonstrate a) how a CBO can implement a KM initiative and b) the effectiveness of the initiative for disseminating the guidelines to support personnel. METHOD: The KM initiative consisted of 12 events about the new guidelines held within the CBO's regional areas. Evaluation of the events was guided by the RE-AIM (reach, efficacy or effectiveness, adoption, implementation, and maintenance) framework. RESULTS: Adoption of the events was high, with 88% of regions hosting an event. Overall fidelity to the event protocol was high among researchers (100.00% ± .00), peers (65% ± 33.74), and staff (70.00% ± 34.96). The events reached 140 support personnel who attended the events. Significant increases in support personnel's self-efficacy and intentions to promote physical activity to people with spinal cord injury were seen at Time 2 but not maintained at Time 3. CONCLUSIONS: Event-based KM initiatives may be an effective strategy for CBOs to disseminate information to support personnel and ensure that KM initiatives are supported by staff and delivered as intended.
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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 | 0.038 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".