“Exploring knowledge-user experiences in integrated knowledge translation: a biomedical investigation of the causes and consequences of food allergy”
Bibliographic record
Abstract
PLAIN ENGLISH SUMMARY: Food allergy is a serious public health problem in Canada and other high-income countries, as it is potentially life threatening and severely impacts the quality of life for individuals and their families. Yet, many questions still remain as to its origins and determinants, and the best practices for treatment. Formed to tackle these very questions, the GET-FACTS research study centers on a novel concept in biomedical research: in order to make this science useful, knowledge creation must include meaningful interactions with knowledge-users. With this, knowledge-users are present at every stage of the research and are crucial, central and equal contributors. This study reflects on the early part of that journey from the perspective of the knowledge-users. We conducted interviews with all non-scientist members of the GET-FACTS steering committee, representing Canadian organizations that deal with patient advocacy and policy with regards to food allergy. Steering committee members had a clear sense that scientists and knowledge-users are equally responsible for putting knowledge into action and the importance of consulting and integrating knowledge-users throughout research. They also have high expectations for the GET-FACTS integrated process; that this model of doing science will create better scientists (e.g. improve communication skills) and make the scientific output more useful and relevant. Our work highlights both the unique contributions that knowledge-users can offer to knowledge creation as well as the challenges of trying to unify members from such different communities (policy/advocacy and biomedical science). There remains a real need to develop more touch points and opportunities for collaboration if true integration is to be achieved. Despite the obstacles, this model can help change the way knowledge is created in the biomedical world. ABSTRACT: ᅟ. BACKGROUND: Despite the burden of food allergic disease many questions remain as to its origins, determinants and best practices for treatment. Formed to tackle these very questions, the GET-FACTS (Genetics, Environment and Therapies: Food Allergy Clinical Tolerance Studies) research study centers around a novel concept in biomedical research: in order to make this science useful, knowledge creation must include meaningful interactions with knowledge-users, known as Integrated Knowledge Translation (IKT). In IKT, knowledge-users are present at every stage of the research and are crucial, central and equal contributors. This paper contributes to this exciting form of research by reflecting on the beginning of that journey from the perspective of the knowledge-users. METHODS: = 9) non-scientist members of the GET-FACTS steering committee, representing Canadian organizations that deal with patient advocacy and policy with regards to food allergy. Transcripts were coded and organized by themes developed both deductively and inductively. RESULTS: Steering committee members indicated a clear sense that scientists and knowledge-users are equally responsible for the translation of knowledge into action and the importance of consulting and integrating knowledge-users throughout research. Overall, these knowledge-users have very high expectations for the GET-FACTS IKT process; they feel that this model of doing science will create better scientists (e.g. improve communication skills) and make the resulting science more useful and relevant; indeed, they reported that this model of knowledge creation can be paradigm shifting. CONCLUSIONS: This study highlights both the unique contributions that knowledge-users can offer to knowledge creation as well as the challenges of trying to unify members from such different communities (policy/advocacy and biomedical science). While our steering committee has a strong conceptual grasp on IKT and vision for their contributions, execution is not without challenges. There remains a real need to develop more touch points and opportunities for collaboration if true integration is to be achieved. Despite the obstacles, the GET-FACTS IKT model represents a new approach to knowledge creation in Canadian biomedical research and can help foster a culture of openness to participant involvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".