Knowledge mobilization to spread awareness of the ‘<scp>F</scp>‐words’ in childhood disability: lessons from a family–researcher partnership
Bibliographic record
Abstract
BACKGROUND: In 2012, two CanChild researchers published an article in Child: Care, Health and Development titled 'The "F-words" in childhood disability: I swear this is how we should think!' Building on the World Health Organization's International Classification of Functioning, Disability and Health (ICF) framework, the article featured key strengths-based ICF themes (i.e. the 'F-words' - Function, Family, Fitness, Fun, Friends and Future). This paper reports on a knowledge mobilization initiative designed to spread awareness of the 'F-words' ideas. METHODS: Families and researchers collaborated to develop, disseminate and evaluate an online awareness video. The video used written descriptions, parents' reflections and their pictures, music and graphics to captivate the audience. Posted on the CanChild website in May 2014, information about the video was distributed via various dissemination strategies and evaluated by tracking its views and through an online survey. RESULTS: After a 2-month evaluation, there were 715 views and 137 survey responses. Of the survey responses, 89% lived in Canada, 55% had not previously heard of the 'F-words', 98% 'extremely liked'/'liked the ideas' and 88% indicated they would share the video. CONCLUSIONS: By creating a short and captivating video, we were able to spread awareness to a wide audience in a short period of time. Engaging families throughout the project was critical to the success of the video. By working together, we hope to continue bridging research and practice and moving the 'F-words' concepts forward one 'word' at a time.
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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 | 0.088 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.025 | 0.029 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.007 | 0.009 |
| 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".