The Development and Evaluation of an Innovative Knowledge Translation Tool about Pediatric Concussion
Bibliographic record
Abstract
Background: Pediatric concussion is a common, yet complex injury caused by a direct or indirect blow to the head. Pediatric concussion places considerable burdens on children, families, and the healthcare system. To minimize this burden, it is essential that patients and families are connected to evidence-based child health information. Despite the plethora of educational resources available about pediatric concussion, uncertainty in how to manage them prevails, particularly among families, and knowing which resources are relevant and accurate is often unclear. This suggests an urgent need for evidence-based strategies that align what is known from research with what is done in health care practice, referred to as knowledge translation strategies. Actively involving patients and families in health care has the potential to optimize knowledge translation. Research indicates that patients and families look for health information online, thus; digital knowledge translation tools are a promising approach to provide complex, child health information. Purpose: The purpose of this dissertation is to understand the information needs and preferences of children who have experienced a concussion and their parents and use these data to develop and evaluate a novel, innovative knowledge translation tool about pediatric concussion. Methods: This dissertation is a multi-phase, multi-method project consisting of four phases and three related papers: (1) an environmental scan to identify and describe Canadian-developed, publicly available Internet resources and Apps about pediatric concussion, (2) a qualitative description study on the experiences, information needs and preferences of children who have experienced a concussion and their parents, and (3) the development and evaluation of a novel, digital knowledge translation tool (interactive infographic) about pediatric concussion. Findings and Conclusions: This dissertation addressed current knowledge gaps in pediatric concussion education tools and actively involved children and parents in the development and evaluation of a digital knowledge translation (KT) tool about pediatric concussion. Results of this research have widespread applications in three key areas. First, the current state of Canadian-developed resources for pediatric concussion found on the Internet and in Apps is described. In paper one, an environmental scan of 600 websites and 200 Apps was conducted. Sixty-seven resources (64 Internet, 3 Apps) met the inclusion criteria. Information gaps in relation to the content, format and target audience of these resources was identified. Most significantly, this scan determined that few resources targeted children and were most commonly available in PDF format. Recommendations for the future development of pediatric concussion resources are provided. Second, this research informed the development of a novel, digital knowledge translation tool (an interactive infographic), about pediatric concussion through integrated knowledge translation methods. In paper two, a qualitative description study interviewing children who have had a concussion, and their parents, was conducted. Four major themes were identified from these interviews: 1) mechanism of injury and concussion symptoms experienced by children, 2) parent concerns, emotions, and health care experience with child’s concussion, 3) concussions affect more than just your head and, 4) health information seeking, and preferences of parents and children related to concussion. Overall, this study demonstrated that children and their parents have unique experiences, information needs and preferences regarding concussion. Third, knowledge translation science was enhanced in terms of the development and testing of a novel, knowledge translation intervention (interactive infographic) for children and parents and building KT research capacity through collaborations with patients, families, and key stakeholders. Paper three describes the development of the interactive infographic, and our findings suggest this infographic was viewed positively by parents and significantly improved parents self-reported confidence in their knowledge of pediatric concussions. The findings of this research make substantial contributions to pediatric concussion research, knowledge translation intervention development and evaluation and patient engagement in research.
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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 | 0.036 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".