Nationwide use, challenges, facilitators, and impact of preceptors in prelicensure clinical nursing education
Bibliographic record
Abstract
Background and objective: Preceptor-facilitated clinical nursing education prevalence information is dated. Information is most often limited to regional baccalaureate programs and provides sparse evidence of its education-related outcomes. The purpose of this study is to describe the nationwide use, structures, facilitators, and challenges of using preceptors in prelicensure clinical education; compare its use by program characteristics; and explore its impact on education-related outcomes.Methods: In this cross-sectional comparative study, prelicensure programs in randomly selected jurisdictions in all four regions of the US were identified and official pass rates obtained. Program administrators completed an online questionnaire about preceptor use, incentives used, challenges, facilitators, and perceived impact on program capacity.Results: Preceptors were used in 73% of the 366 responding programs. Prevalence rates ranged from 25% to 87% by program type and from 64% to 86% by region. Programs’ NCLEX-RN® pass rates and perceived impact on program capacity did not differ by use of preceptors. Most respondents indicated there was no impact although one-fifth perceived moderate to high impact. The top five challenges and facilitators to preceptor use were identified. Programs used a variety of preceptor incentives, ranging from 62% using informal recognition to 7% providing some type of financial compensation.Conclusions: Most programs use preceptors with differences by program type and region. Designating resources to enhance preceptor orientation and preceptor-student-faculty communications may be useful, as well as identifying the challenges and facilitators. While a variety of preceptor incentives are available, few offer direct monetary compensation. Regional preceptor incentive data provide useful benchmarks. With high rates of use in some sectors and yet no demonstrable influence on pass rates, closer scrutiny of the quality of preceptor-facilitated educational experiences and associated outcomes are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".