Dietitian Preceptor Knowledge, Skills, Attitudes, and Training: Key Informant Perceptions
Bibliographic record
Abstract
PURPOSE: Through consultation with Canadian dietitian informants, we aimed to identify the desired knowledge, skills, and attitudes (KSA) for preceptors, training opportunities, and the barriers that prevent preceptor training. METHODS: In this qualitative study, an open-ended survey was sent electronically to 100 key informants across Canada. Informants had experience as preceptors or with dietitian preceptors. Informants were asked to reflect upon the desired KSA, training needs, and barriers to training for dietitian preceptors. Categories of responses under each of these headings were developed on the basis of informants' responses. RESULTS: Forty-nine key informants completed the survey, for a 49% response rate. Of the respondents, 41% (20/49) were in clinical practice and 35% (17/49) worked in community/public health areas. The knowledge and skills domains consisted of themes related to teaching and learning, including assessing, planning, and evaluating. Attitudes expressed included considering learners as colleagues and the training of learners as a professional responsibility. Perceived barriers to training preceptors included workload demands and a lack of recognition from peers and employers for this work. Dietitian preceptor training opportunities ranged from no training to formal programs. CONCLUSIONS: These findings are integral to the basic understanding of the desired KSA and training needs of Canadian dietitian preceptors.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".