Delivering Evidence-Based Online Concussion Education to Medical and Healthcare Professionals: The Concussion Awareness Training Tool (CATT)
Bibliographic record
Abstract
Background. Medical and healthcare professionals report an important gap in their training and knowledge on concussion diagnosis and management. The Concussion Awareness Training Tool (CATT) for medical professionals provides evidenced-based training and resources, representing an important effort to fill this gap. The goal of the current article was to summarize and describe the general uptake of the 2018 relaunch of the CATT for medical professionals and to present results of a quality assurance/quality improvement (QA/QI) assessment including qualitative feedback from medical and healthcare professionals. Methodology. Tracking completions via certificates and Google Analytics were used to measure uptake over the first two years following the 2018 relaunch and promotion of CATT for medical professionals. Medical and healthcare professionals who had completed the CATT from the time of the relaunch on June 11, 2018, to July 31, 2019, were invited via e-mail to participate in the survey-based QA/QI assessment. Both quantitative and qualitative data were collected. Results. Year 1 saw 8,072 pageviews for the CATT for medical professionals landing page, increasing to 9,382 in Year 2. Eighty-nine medical and healthcare professionals who had completed the CATT for medical professionals participated in the QA/QI assessment. Results showed that 85% of respondents reported learning new information about concussion; 73% reported changing the way they diagnose, treat, or manage concussion; and 71% reported recommending the CATT to colleagues. Qualitative data also indicated highly favourable opinions and experiences. Conclusions. The CATT for medical professionals has demonstrated promise as a tool to promote knowledge translation practice and help fill the gap in concussion training and knowledge reported by medical and healthcare professionals.
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 | 0.023 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".