An Innovative, Strengths-Based, Peer Mentoring Approach to Professional Development for Registered Dietitians
Bibliographic record
Abstract
The Professional Development Network (PDN) program was implemented to enhance mentoring and learning opportunities for dietitians at a multisite health care organization. Program development, implementation, and evaluation were carried out by a Professional Practice Council composed of dietitians in the organization. An exploratory evaluation was conducted after the first year of PDN implementation. Evaluation data were collected from an online survey containing open- and closed-ended questions and PDN documents submitted by dietitians. Data were analyzed with descriptive statistics and thematic analysis. Survey results indicate the PDN provided a mechanism for dietitians to learn from each other, apply learning to their career development, reflect on their strengths, and connect with others in the department. Analysis of PDN documents showed that dietitians pursued learning related to clinical practice, technology, private practice, and research. Mentoring interactions were also described by participants within PDN documents. Findings from this study demonstrate how multiple frameworks from academic literature can be integrated to create a professional development program in a dietetics practice environment. Evaluation results from this study may provide useful insights for others interested in implementing professional development programming.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".