Developing self-efficacy as nurse preceptors: a qualitative descriptive study
Bibliographic record
Abstract
Preceptors are expected to facilitate students’ learning and the transition of new graduate nurses in the clinical area but may not be fully prepared for the role. The literature abounds with evidence supportive of considerable preceptor development yet researchers have been slow to explore preceptors’ perceptions of their capabilities and how they influence their willingness and behaviors. This qualitative descriptive study explores nurse preceptors’ perceptions of their self-efficacy when precepting senior practicum students. Albert Bandura’s self-efficacy theory guided this study. Individual semi-structured interviews were conducted with eight participants. Qualitative content analysis was used to analyze the data. Findings included two major themes, experiencing self-efficacy as a nurse preceptor and developing self-efficacy as a nurse preceptor. Previous preceptor experience, years of clinical practice experience, practicum experience as students and workplace supports were identified as salient sources of preceptor self-efficacy. Findings provided insight into the current state of self-efficacy in the preceptor role and contributed to addressing a gap in the literature. Findings have implications for strategies for development of self-efficacy and provided insight into other potential resources for supporting nurse preceptors in the development of their knowledge and behavior. The findings can inform the groundwork for research on further consideration for the implementation of structured transition programs in Canada.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".