The need for a food allergy educator program for allied healthcare professionals in Canada
Bibliographic record
Abstract
Owing to a collaborative approach to patient care, and a paucity of allergists in Canada, there is a need to develop a food allergy educational program for allied health care professionals in Canada. Such programs already exist in the United States and Britain. Herein, we describe the outcomes of recent conference proceedings to inform the educational needs for such a program. As part of the 76th Annual Meeting of the Canadian Society of Allergy and Clinical Immunology (CSACI), held virtually due to the COVID-19 pandemic, we hosted a virtual workshop on the need for a food allergy educator program for Canadian allied health professionals. This workshop was co-developed with the CSACI and an industry partner, and featured allergy specialist dietitians. Attendance was open to all conference delegates, and to allied health professionals. As part of the registration process, registrants posed diverse food allergy-related questions, ranging from how to use an epinephrine autoinjector, to daily management and, how to cure food allergy. A national food allergy educator program will empower both allergy and non-allergy specialist healthcare professionals to appropriately counsel patients. This virtually-delivered program will begin to close a gap in healthcare access resulting from the geographic size of Canada, as it will enhance allied healthcare providers' confidence to provide evidence-based food allergy care appropriately for those with food allergy.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.025 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 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".