“The Year of Give”: Dietitians’ Experiences in Nova Scotia during the COVID-19 Pandemic
Bibliographic record
Abstract
Purpose: Researchers explored the roles and responsibilities of dietitians in Nova Scotia during the first three waves of the COVID-19 pandemic. Methods: Twenty-two dietitians who had completed a survey agreed to participate in interviews to elucidate their views. Two group and 13 individual interviews were held with dietitians from diverse work sectors during the summer of 2021. Interviews were transcribed verbatim, organized using NVivo software, and analyzed thematically. The Social Ecological Model of Health framed the study by exploring the individual, interpersonal, community, organizational, and policy aspects of practice. Results: Major themes reflected constantly changing responsibilities, dealing with the impact of cancelled or delayed health services, coping with stress, and valuing/devaluing dietetics work. Professional education needs and conditions for successful practice were also identified. Conclusions: Dietitians’ experiences varied depending on the sector in which they worked, with significant role changes unique to each sector. Emergency preparedness, infection control, health equity, and mental health/self-care were identified as areas for further education and development. The breadth of foundational dietetics training enabled dietitians to take on diverse responsibilities and leadership roles. This supports calls to enhance dietitians’ scope of practice in Nova Scotia.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".