A situational analysis of registered dietitians' participation in network marketing
Bibliographic record
Abstract
BACKGROUND: Registered dietitians (RDs) are allied health professionals with advanced training in nutrition and food science. To practice, RDs must maintain registration with the regulatory body in their jurisdiction. METHODS: We conducted a situational analysis to better understand: (i) RDs participation as independent sales consultants (ISCs) for network marketing companies and (ii) the role of regulatory bodies in overseeing network marketing participation among RDs. We conducted semi-structured interviews with individuals who had, within the past 5 years, concurrently been an RD and an ISC, and with three representatives of non-RD regulatory bodies in the province of Ontario. Other sources of discursive data included relevant articles published in academic journals and in the mainstream media, documentary series and circulating memes. RESULTS: Our results are depicted in three maps (ordered situational, arenas and positional). Overall, much of what was highlighted in the reviewed articles and expressed in the analytic maps about network marketing remained unsaid in RD interviews (n = 8). CONCLUSIONS: RDs who participate in network marketing were often able to achieve a level of personal fulfilment that appeared unattainable through their professional work alone. However, the stigma of network marketing participation appeared to diminish the benefits of ISC work. Consistent, clear guidelines from RD regulatory bodies are desired by RD/ISCs.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".