Dietitians as innovators: a deductive-inductive qualitative analysis
Bibliographic record
Abstract
BACKGROUND: Frontline health professionals are well-placed to develop and implement beneficial innovations. Evidence supports the clinical and financial benefits of Registered Dietitian (RD)-led improvement initiatives, but we know little about how RDs perceive of innovation or of themselves as innovators. The objectives of the study were to gain an understanding of: 1) how RDs define innovation; 2) who RDs perceive as innovative; 3) whether RDs feel prepared to innovate, and; 4) to what extent work context impacts RDs' capacity to innovate at work. METHODS: All RDs employed in Canada were eligible to participate. Semi-structured interviews were conducted and a deductive-inductive approach was applied to qualitative analysis. Specifically, Scott & Bruce's (1994) Path Model of Individual Innovation in the Workplace was first applied as a coding structure. RESULTS: Respondents (n = 18) exhibited a pro-innovation disposition and a gendered perception of innovation. Few felt their preparatory education prepared them to be innovators in the workplace. All components of Scott & Bruce's model were supported. Inductive codes were categorized into five themes, including: benefits, dietetics-specific, health care system, technology and individual characteristics. CONCLUSIONS: Researchers have previously raised concerns about gendered perceptions of innovation; our results support the legitimacy of these concerns. RDs reported entering the workforce unprepared to be innovative. While the applicability of Scott & Bruce's model among RDs was confirmed, deficits in the model were noted beyond what would be expected due to context.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".