Bibliographic record
Abstract
The prevalence of food allergies and intolerances among adults, also known as food hypersensitivities (FHSs), are on the rise. There is a gap in health services for this population. Increasing competencies of health care providers (HCPs) can start with an understanding of the problem from the perspective of those who live with FHSs. A purposive, asset-based sample of four women, who perceived themselves as living well with FHSs, were interviewed in this narrative inquiry. Participants were invited to reflect on their first-hand experiences of FHSs, their perceptions of dietetics care and their own journeys toward well-being. Data collection included an information gathering questionnaire (Appendix A), individual semi-structured interviews and participants’ drawings which they illustrated to assist storytelling. While the onset of an FHS brought uncertainty into the lives of participants, this article focuses on how learning themes reveal the importance of incorporating a more robust understanding of adult education in dietetic interventions through participants’ stories of moving toward well-being. The findings suggest that when supported by determinants of health, such as education and social support, transformative learning can be an important tool to mitigate the uncertainties related to the experience of an FHS. The research reveals that adult educators, with their grasp of adult learning processes and community engagement, could be an asset to the interprofessional health care team and improve the provision of person-centered care.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".