The Who, What, When, and How: Of Choosing a Dietetics Career
Bibliographic record
Abstract
PURPOSE: We describe factors influencing the career choices of students enrolled in Canadian dietetics programs. METHODS: A survey was administered, in class or online, to core first- and fourth-year classes in seven dietetics programs in various provinces (n=397). Data were analyzed with the use of descriptive statistics. Chi-square testing for independence established significant relationships. RESULTS: Students ranked personal satisfaction, job security, and a professional career as important general career outcomes. These factors were also perceived to be attainable through a dietetics career. The majority of students chose dietetics while they were enrolled in a post-secondary degree program (44%), were primarily influenced by family members (54%), and based their choice on information acquired through the media (50%). Motivations for choosing dietetics included an interest in nutrition (91%) and health (90%), and a desire to help others (82%). Younger students placed more importance on economic rewards and having a position of authority than did older students. Older students identified personal satisfaction as more important in career selection than did younger students. Female respondents placed higher value on job flexibility than did their male counterparts. CONCLUSIONS: Career choice is based on a variety of internal and external factors. Opportunities exist for strategic recruitment efforts by educators and the profession.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".