Evaluating the effectiveness of let’s talk period’s high school educational outreach program: A pilot study
Bibliographic record
Abstract
INTRODUCTION: Menorrhagia impacts ~40% of adolescent females, with about half having an underlying bleeding disorder, most commonly von Willebrand Disease (VWD). VWD affects ~1 in 1000 individuals, though many are unaware of their condition. Let's Talk Period (LTP) is an online knowledge translation platform aimed at increasing awareness of bleeding disorders symptoms, with a validated self-administered bleeding assessment tool (Self-BAT). AIM: To evaluate the effectiveness of the LTP high school outreach program in Grade 9 girls' health classes quantitatively, using baseline, post-presentation, and follow-up quiz scores, and qualitatively, with student and teacher feedback forms. METHODS: The 75-minute in-class presentations, developed in alignment with the 2015 Ontario Curriculum for Grade 9 Health and Physical Activity, were led by a haemophilia nurse, clinical research assistant, and undergraduate student from the LTP team. Students completed baseline, post-presentation, and 4-6-week follow-up Kahoot quizzes featuring the same nine questions to evaluate change in knowledge levels and retention. Both student and teacher feedback were collected. RESULTS: There was a significant increase (p < 0.001) from baseline to post-presentation scores, with a significant gain in knowledge, for all questions (p < 0.01). Students found content related to the basics and management of menstruation to be most interesting. Many had constructive feedback on how the presentation method could be improved. On average, the presentations were rated an 8.6 of 10 by students and 8.75 of 10 by teachers. CONCLUSION: The LTP high school outreach program effectively increases student knowledge of menorrhagia and bleeding disorders. It was well-received by students and staff alike.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".