Enhancing quality and safety in clinical teaching: Statewide live continuing education program for adjunct clinical nursing faculty
Bibliographic record
Abstract
The nursing faculty shortage is being filled by adjunct clinical faculty with no classroom teaching experience. These novice faculty members must undergo a socialization and orientation process (onboarding), when transitioning into the academic environment. To support orientation, the Live Continuing Education Program for Adjunct Clinical Nursing Faculty (LCEP-ACNF), a competency-based 4.0-hour continuing education unit program for novice clinical faculty was used. In this study, the LCEP-ACNF was tested in a statewide sample of clinical faculty. For this mixed-methods study a convenience sample of faculty members (N = 312) from all nursing programs in one northeast state was recruited. All 312 participants completed pretest competency-based evaluation and a demographics sheet. Participants’ (N = 312; n = 162) posttest scores were significantly higher than their pretest scores (Z = 11.10, p < .01). Eight interviews were conducted and the themes emerged were, communication with other faculty members on clinical teaching, orientation strategies, student evaluation and feedback strategies, and mentorship issues for novice clinical faculty. Evaluation results for the LCEP-ACNF were overall positive, including the need for more continuing education offerings, mentorship, and teaching strategies. The results suggest an increased need for clinical faculty development and orientation, a need for developing the clinical coordinator role and mentorship for all novice clinical faculty. Lastly, the LCEP-ACNF should be offered twice a year, regionally and nationally.
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 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 | 0.003 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".