Online preparation programs for part-time clinical nursing instructors: A qualitative case study
Bibliographic record
Abstract
Background/Objective: The global nursing shortage is worsened by critical nursing faculty shortages that constrain educational capacity, resulting in thousands of qualified applications being rejected annually. Programs increasingly rely on clinically expert registered nurses (RNs) as part-time clinical nursing instructors (CNIs), who often transition to teaching with minimal pedagogical preparation. This gap between clinical expertise and educational competency affects instructor confidence, teaching effectiveness, and student outcomes. This qualitative case study aimed to describe RNs' experiences with an online Academic Clinical Nurse Educator Preparation Program (ACNEPP) and examine its influence on their transition from clinical experts to academic CNIs. Methods: A single instrumental case study design employing the constructivist paradigm was utilized. Six RNs who completed the online ACNEPP within the previous three years and worked as CNIs were recruited through purposeful sampling. Data collection included semi-structured interviews, demographic questionnaires, and program material reviews. Reflexive Thematic Analysis guided data analysis, with Benner's novice-to-expert theory as the theoretical framework. Results: Three main themes emerged: RN to CNI role transition challenges, online ACNEPP design effectiveness, and valuable program content. Participants experienced significant transition difficulties including inadequate institutional preparation, unclear role expectations, and insufficient mentorship support. The online program's asynchronous, self-paced design proved highly accessible for working nurses. Participants valued practical teaching tools, clinical teaching strategies, and pedagogical content that enhanced teaching practices, including improved debriefing techniques, structured feedback provision, and student orientation strategies. Conclusions: This study illuminates the potential of structured online professional development in supporting the challenging transition from clinical expert to CNI. Findings support developing self-paced, flexible programs offering practical, immediately applicable content. Educational institutions must prioritize formal orientation programs, establish mentorship programs, and provide ongoing professional development which may enhance teaching quality, improve instructor confidence, increase retention rates, and ultimately expand nursing education capacity.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".