Bibliographic record
Abstract
ABSTRACT Objectives: Although experiences of burnout are well documented among some health professionals, there is limited research that explores similar experiences among dietitians. This study aims (1) to describe the varied qualitative dimensions of burnout that are particular to dietitians and (2) to identify the factors that might be deemed protective against burnout. Methods: Fourteen dietitians were recruited from a larger quantitative study that assessed prevalence of burnout in Ontario, Canada using the Maslach Burnout Inventory (MBI). Those who completed the MBI were invited to participate in two phenomenological interviews. Transcribed interviews were analyzed by naïve readings and identified meaning units with a larger team for increased rigor and trustworthiness. Results: Dietitians describe burnout as having bodily and overall health consequences. Both social/professional relationships and dietitians’ passion for their work contributed to experiences of burnout and resilience. Opportunities for continued professional growth and change were contributing factors for resilience. Implications & Conclusions: This study contributes to the limited body of knowledge on dietitians’ lived experiences of burnout and resilience. The findings have implications for those involved in the education and training of student dietitians, and for those in a position to offer support to dietitians who are struggling with job stress. In the context of fostering resilience, a preventative approach to dietetic education is explored with the intention to protect future practitioners from burnout.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| 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".