An Environmental Scan of Kt Tools About Pediatric Concussion in Canada : Protocol
Bibliographic record
Abstract
Background: In North America, more than 650 000 children present to hospital emergency departments (ED) annually, for concussions. Concussion, is defined as a complex pathophysiologic process affecting the brain, induced by biomechanical forces due to either a direct or indirect blow, resulting in the impairment of neurologic function with clinical symptoms. Signs and symptoms may include headache, nausea, sensitivity to light or noise, blurred vision and in some instances loss of consciousness. While most children recover, concussions may lead to prolonged recovery, lasting several months to over a year in rare cases. While concussion organizations in Canada do provide accurate information for health consumers about pediatric concussions, the information can be complex and difficult to understand. Lack of accurate, user-friendly, and understandable information about pediatric concussion is problematic and may result in unnecessary ED visits and poor outcomes for children. To maximize health system resources and improve patient outcomes, it is imperative that research knowledge translates into action u2013 a process called knowledge translation (KT). KT for healthcare consumers uses tools, (e.g. pamphlets, eBooks) to present research-based information in user-friendly language and formats. To improve KT of pediatric concussion, an environmental scan (ES) will be conducted to develop an understanding of what KT tools about pediatric concussion are currently available in Canada. Methods: An ES will identify what information tools are currently available in Canada about pediatric concussion. The research question underpinning this ES is: What English-language information/KT tools about pediatric concussion are currently available in Canada? First, KT tools and information resources about pediatric concussion in Canada will be identified via Google search engine and Internet-based app stores (Apple and Google Play). Second, key informants at concussion organizations (e.g. Parachute Canada) will be identified and consulted to solicit known examples of relevant KT tools that are not available through our searches and to further inquire about the tools that are available. Inclusion criteria will be purposefully broad in order to ensure the full breadth of evidence is considered (e.g. no specific population, no specific type of tool). Planned Analysis: Data tables will be developed. Content analysis will be used to extract desired information from both verbal and written sources by systematically and objectively identifying specific characteristics of the material (e.g. information format, audience).Anticipated Outcomes: This ES will generate three outcomes: (i) identify and synthesize evidence about existing KT tools for pediatric concussion; (ii) identify gaps among these tools; and (iii) provide a detailed taxonomy of educational tools about pediatric concussion. These findings will be crucial to the subsequent phases of this research to develop a new innovative KT tool about pediatric concussion that addresses the gaps identified in this scan.
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 | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.097 | 0.016 |
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".