Developing nurse preceptor competency domain guide tool: A Delphi study
Bibliographic record
Abstract
Background and objective: One of the strategies used to prepare novice nurses for their professional journeys in clinical practice is by implementing the preceptorship teaching and learning model. Competencies such as knowledge, experience, abilities, and attributes need to be measured to ensure the desired outcomes of the preceptorship are achieved and consistent. This study aimed to develop a nurse preceptor competency domain guide tool at a tertiary hospital in Oman.Methods: Three-round Delphi iterative design with experts was used to develop the nurse preceptor competency domain guide tool. Following standard measures, eight expert opinions were combined until a group consensus was achieved. The level of consensus within the expert panel was defined as ≥ 75% scoring of items were selected as an essential required competency/item.Results: Eight experts from a main tertiary hospital were included in the panel. Five core competency domains and five subdomains were identified and considered to be relevant for nurse preceptors at the hospital with consensus levels varying from 75% to 100%. A total of 83 descriptive items were identified for the competency guide tool.Conclusions: This study found that the main core competency domains of the tool that nurse preceptors should acquire to be competent preceptors are inter-professional communication skills, appropriate teaching strategies, time management skills, building a learning atmosphere, and coaching critical thinking. This tool would improve nurse preceptors’ performance and equip them with the required prerequisite competencies to professionally start their journey in clinical practices. Follow-up research on tool implementation is highly recommended to evaluate its effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".