An environmental scan of Canadian Internet resources and Apps about pediatric concussion. (Preprint)
Bibliographic record
Abstract
BACKGROUND Concussions are complex injuries affecting millions of children worldwide. Various organizations have developed educational supports about childhood concussions in a variety of formats including handouts, training tools and videos. Despite the plethora of resources available, uncertainty in how to manage childhood concussions prevails, and knowing which resources are relevant and accurate can be confusing. For many Canadians the Internet and smartphone applications are easily accessible and offer information pertinent to one’s health and well-being, including those suffering from concussion. However, research is needed to discover information gaps in relation to these Internet resources and Apps to reduce future resource redundancies and improve knowledge translation, ultimately improving concussion care and outcomes for children and families. OBJECTIVE The objective of our study was to identify Canadian-based Internet resources and Apps for pediatric concussion, extract information about each resource to identify gaps, and assess these resources for suitability. METHODS We conducted an environmental scan of Canadian-based Internet resources and Apps on pediatric concussion. Three main sources were sequentially searched: The Internet (Google) and two App stores (Apple, Google Play). Interviews with key informants from Canadian concussion organizations were conducted to further inquire about resources. Resources meeting the inclusion criteria were evaluated using the Suitability Assessment of Materials (SAM). RESULTS 300 Internet websites and 200 apps were searched. A total of 53 resources (51 web-based resources and 2 Apps) met the inclusion criteria. Target audiences included parents (n=11), health care professionals (n=6), teachers (n=5), coaches (n=3), and youth (n=2). Twenty-six resources did not have a specified target audience. Symptoms (n=35), treatment (n=28) and return-to-play (n=24) information was the most common. The most common formats were PDFs (n=20) and infographics (n=8). SAM scores ranged from 36.8% to 97.2%. CONCLUSIONS A limited number of resources were developed specifically for children or youth who have sustained concussions, and those that did were sport specific. Only one resource shared a patient or family experience with concussion. Future resources aiming to improve the knowledge and awareness of pediatric concussions require more inclusivity beyond the athletic community. Additionally, the knowledge and perspectives of those using these resources should be incorporated into their development to enhance relevance, cultural appropriateness and sensemaking. More creative and innovative formats may also enhance the overall usefulness and effectiveness of these resources.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.030 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".