An Initiative to Increase Awareness of Sickle Cell Disease in Queen’s Nursing Faculty and Students.
Bibliographic record
Abstract
Introduction: Sickle Cell Disease (SCD) significantly impacts patient outcomes and healthcare systems yet remains under-represented in health education. This study addresses the educational gaps among nursing faculty and students to enhance awareness and contribute to health equity. Methods: An awareness event was organised in the School of Nursing lobby, facilitated by the distribution of educational materials and promotional items provided by the Sickle Cell Awareness Group of Ontario. Participants, including faculty and students, engaged in a quiz comprising three questions designed to assess their knowledge of SCD. Responses were collected anonymously using Qualtrics, accessed via QR codes. Results: Of the 86 participants surveyed, including 12 faculty members and 74 students, 82.5% correctly answered all quiz questions. Misconceptions persisted regarding the exclusivity of the disease to certain populations and the protective effects of the sickle cell trait. The results indicate a solid foundational understanding of SCD among participants. Discussion: The initiative successfully raised awareness and pinpointed crucial educational deficiencies within nursing curricula. It underscored the necessity of integrating comprehensive SCD education to prepare healthcare providers better. Future research should explore the efficacy of specific educational interventions in improving knowledge retention and patient care outcomes, possibly extending to interprofessional teams for a broader impact.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".