Narrative as a knowledge translation tool for facilitating impact: Translating physical activity knowledge to disabled people and health professionals.
Bibliographic record
Abstract
OBJECTIVE: Theoretically informed by narrative inquiry, this article examines the utility of stories as a possible tool for disseminating synthesized physical activity knowledge to adults with spinal cord injury (SCI) and health care professionals (HCPs) working with this population. It is the first research to systematically examine in this context the use of narratives as a knowledge translation tool. METHOD: Forty-three participants (15 adults with SCI; 13 peer mentors with SCI; and 15 HCPs) individually listened to an evidence-based story set in a rehabilitation hospital about the process of becoming physically active following SCI. Individual telephone interviews were conducted to examine participants' perceptions of the story. Qualitative data were analyzed using a thematic analysis. RESULTS: Five themes were inductively identified: (a) effective communication, (b) narrative authenticity, (c) credible messengers, (d) narrative format, and (e) narrative as a form of action. Together, the themes reveal that the story had utility, the various attributes that help explain why this is case, how the utility might be maximized, what the stories could do on and for people, and how the narratives can be used to support behavior change. CONCLUSIONS: The article advances knowledge by revealing the value of narrative as a means for disseminating evidence-based information to people with SCI and to HCPs. It also reveals that stories can be used to facilitate dialogue, teach, remind, reassure, and reinvigorate people. This article is a resource for enabling knowledge to be more effectively shared to different audiences and applying what we know in practice to help people live meaningful lives.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".