Supporting professional development for early career pre-tenure nursing faculty using Narrative Reflective Process
Bibliographic record
Abstract
Background: Early career, pre-tenure nursing faculty face significant challenges as they navigate the demands of academia, particularly during their first year. While there is growing research about pre-tenure faculty needs, specific insights into early career nursing faculty, especially in Canada, is limited. It is also not known how professional development strategies such as Schwind’s Narrative Reflective Process (NRP) could facilitate integration into academia and support professional growth. The purpose of this article is to explore the application of NRP as a professional development tool for nursing faculty during their first year of a tenure-track position.Methods: At a large urban school of nursing in Canada, four pre-tenure nursing faculty members in their first year of appointment engaged in a series of collaborative, professional development exercises guided by a senior faculty member using NRP. A composite reflection was analyzed using qualitative content analysis.Results: Six themes emerged from the faculty members’ experiences using NRP: needing time to reflect, learning process, creative process, sharing experiences and finding our unique path, experiencing emotions, and looking forward.Discussion: This article highlights the experiences of NRP on early career nursing faculty. Findings reveal the need for dedicated reflection time to foster emotional engagement. Creative activities deepened insights into faculty identities, while sharing experiences fostered community and collaboration. Faculty expressed enthusiasm for applying NRP in future educational and research contexts, recognizing its value for ongoing professional development. Conclusion: NRP is a promising tool to promote professional development in early career tenure-track nursing faculty in academia.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".