Supporting nurse practitioner education: Preceptorship recruitment and retention
Bibliographic record
Abstract
OBJECTIVES: Clinical experience is an essential component of nurse practitioner (NP) education that relies heavily on preceptors. Recruitment and retention of preceptors is challenging due to many variables that can affect NP education and practice. We surveyed Canadian NP programs to understand their preceptorship structures, how they support preceptorship, and to identify gaps and challenges to recruitment and retention of preceptors. METHODS: An 18-item survey, developed by the NP Education Interest Group, was distributed to 24 universities across 10 Canadian provinces. Construct validity and reliability was assessed by experienced NPs and NP faculty. Data were analyzed using relative frequency statistics and thematic analysis. Participants consisted of administrative staff and/or faculty designated as responsible for recruitment and retention of NP preceptors. RESULTS: Seventeen returned surveys were analyzed and demonstrated more similarities than differences across Canada's NP programs, particularly related to barriers affecting recruitment and retention of preceptors. The findings identified NP programs have too many students for the number of available clinical sites/preceptors, resulting in overutilization, burnout, or refusal to take students. Competition with other health disciplines for clinical placements was identified as a challenge to placements. Respondents commented they lack time to recruit, provide follow-up, offer support, or seek preceptors' feedback due to competing work demands. They identified the need for standardized funding for preceptor remuneration and recognition across the country. CONCLUSION: The findings suggest the need for exploring a wider intraprofessional collaboration among graduate NP programs/faculty, clinical placement sites, and NPs to facilitate the recruitment and retention of 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 | 0.039 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".