A Focused Ethnography of Tenure-Track PhD-Prepared Nursing Faculty Members’ Teaching Experiences
Bibliographic record
Abstract
Introduction: New faculty’s experiences in a tenure track position have been reported to be stressful and retention of new faculty can be difficult in the competitive academic climate. However, research literature on this topic is predominantly American based. A focused ethnography was undertaken to understand the experience of new PhD-prepared nursing faculty in Canada more fully. Purpose: The purpose of this article is to present findings about the teaching experiences from a research study examining the experiences of 17 new PhD-prepared, tenure-track nursing faculty in their role from nine Canadian universities representing various provinces and regions. Method: A focused ethnography method was used to examine these experiences. Semi-structured virtual interviews of participants were conducted between March 2021 and April 2022. Recruitment stopped when no new information was being found or data saturation was reached. Lincoln and Guba’s (1985) criteria for rigour in qualitative methods underpinned this work. Qualitative methods such as constant comparison of data, memoing, and triangulation of data were integral to enhancing rigor. Roper and Shapiro’s (2000) steps for thematic analysis were followed and Quirkos qualitative software was used to store interviews, memos, and theme development in one secure format. Results: The central themes elucidated from participants’ responses in this study were mentoring, joys and challenges of teaching, institutional supports and processes, and managing a heavy workload. Even if experienced with teaching in the academic setting, participants wanted a mentor to aid with socialization to the institution. Many new faculty reported that time management and heavy workloads were significant challenges during the initial period, but they were developing strategies to aid with balancing the multiple academic demands and personal life demands. Participants expressed joy with the act of teaching and sharing of knowledge even if some participants experienced incivility or bullying from students. Experience with teaching graduate classes varied from institution to institution although most participants had begun to supervise graduate students or even have their student graduate, which was very fulfilling. The impact of the COVID-19 pandemic on these new tenure-track faculty’s teaching experiences was also explored. Conclusion: Implications for practice and potential faculty supports are proposed based upon the findings of this research, in addition to key future research directions.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".