What Practice Issues Over 25 Years Most Interest Registered Dietitians? Survey and Interview Results
Bibliographic record
Abstract
) identify possible key informants. An online bilingual survey was conducted in 2018, with follow-up phone interviews among interested respondents. Survey content was organised as 12 major topics. Respondents were invited via a Dietitians of Canada (DC) newsletter, Facebook groups, and at the DC national conference. Survey data, including respondent-generated topics of interest and interview content, were descriptively analyzed. The online survey garnered 360 responses; 332 (92%) completed more than 10% of the survey and were interested in history. Detailed responses were analyzed (296 English; 36 French); 51 were interviewed. An online timeline was the most preferred format (79%). Review of the rise in technology and obesity, aging, supermarket registered dietitians (RDs), the local/organic movement, Practice-based Evidence in Nutrition (PEN), the changes in training models and scope of practice, public awareness of the profession, and advocacy and unique career paths were of most interest (≥ 50% of respondents). These results confirm interest in the recent history of the profession among RDs and provide guidance on preferred format and topics for further work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".